Brain microstructural changes already occur in the earliest phases of Alzheimer’s disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Reduced Acquisitions (AMURA) as imaging markers for capturing such tissue modifications.Tract-based spatial statistics (TBSS) and support vector machines (SVMs) based on different measures were exploited to distinguish between amyloid-beta/tau negative (A $beta $ -/tau-) and A $beta $ +/tau+ or A $beta $ +/tau- subjects. Moreover, eXplainable Artificial Intelligence (XAI) was used to highlight the most influential features in the SVMs classifications and to validate the results by seeing the explanations’ recurrence across different methods.TBSS analysis revealed significant differences between A $beta $ -/tau- and other groups in line with the literature. The best SVM classification performance reached an accuracy of 0.73 by using advanced measures compared to more standard ones. Moreover, the explainability analysis suggested the results’ stability and the central role of the cingulum to show early sign of AD.By relying on SVM classification and XAI interpretation of the outcomes, AMURA indices can be considered viable markers for amyloid and tau pathology. Clinical impact: This pre-clinical research revealed AMURA indices as viable imaging markers for timely AD diagnosis by acquiring clinically feasible dMR images, with advantages compared to more invasive methods employed nowadays.
正如弥散磁共振成像（dMRI）文献所证实的那样，阿尔茨海默病（AD）的早期阶段已经出现大脑微结构变化。本研究探讨了新型 dMRI 表观测量（Apparent Measures Using Reduced Acquisitions，AMURA）作为成像标记捕捉此类组织变化的潜力。研究人员利用基于不同测量方法的肽段空间统计（Tract-based spatial statistics，TBSS）和支持向量机（Support vector machines，SVMs）来区分淀粉样蛋白-β/tau 阴性（A $beta $ -/tau-）和 A $beta $ +/tau+ 或 A $beta $ +/tau- 受试者。此外，eXplainable 人工智能（XAI）被用来突出 SVMs 分类中最有影响力的特征，并通过查看不同方法中解释的重复性来验证结果。与更标准的方法相比，使用高级方法的 SVM 分类准确率达到了 0.73。此外，可解释性分析表明了结果的稳定性以及蝶鞍在显示 AD 早期迹象方面的核心作用。通过依赖 SVM 分类和 XAI 结果解释，AMURA 指数可被视为淀粉样蛋白和 tau 病理学的可行标记。临床影响：这项临床前研究通过获取临床上可行的dMR图像，揭示了AMURA指数是及时诊断AD的可行成像标记物，与目前采用的更具侵入性的方法相比具有优势。
{"title":"XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer’s Disease","authors":"Lorenza Brusini;Federica Cruciani;Gabriele Dall’Aglio;Tommaso Zajac;Ilaria Boscolo Galazzo;Mauro Zucchelli;Gloria Menegaz","doi":"10.1109/JTEHM.2024.3430035","DOIUrl":"10.1109/JTEHM.2024.3430035","url":null,"abstract":"Brain microstructural changes already occur in the earliest phases of Alzheimer’s disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Reduced Acquisitions (AMURA) as imaging markers for capturing such tissue modifications.Tract-based spatial statistics (TBSS) and support vector machines (SVMs) based on different measures were exploited to distinguish between amyloid-beta/tau negative (A\u0000<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>\u0000-/tau-) and A\u0000<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>\u0000+/tau+ or A\u0000<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>\u0000+/tau- subjects. Moreover, eXplainable Artificial Intelligence (XAI) was used to highlight the most influential features in the SVMs classifications and to validate the results by seeing the explanations’ recurrence across different methods.TBSS analysis revealed significant differences between A\u0000<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>\u0000-/tau- and other groups in line with the literature. The best SVM classification performance reached an accuracy of 0.73 by using advanced measures compared to more standard ones. Moreover, the explainability analysis suggested the results’ stability and the central role of the cingulum to show early sign of AD.By relying on SVM classification and XAI interpretation of the outcomes, AMURA indices can be considered viable markers for amyloid and tau pathology. Clinical impact: This pre-clinical research revealed AMURA indices as viable imaging markers for timely AD diagnosis by acquiring clinically feasible dMR images, with advantages compared to more invasive methods employed nowadays.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"569-579"},"PeriodicalIF":3.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-16DOI: 10.1109/JTEHM.2024.3429422
Hyungsoo Lim;Dong Ah Shin;Jaehoon Sim;Jaeheung Park;Taegyun Kim;Kyung Su Kim;Gil Joon Suh;Jung Chan Lee
Objective: This study introduces a novel system that can simulate diverse mechanical properties of the human chest to enhance the experience of CPR training by reflecting realistic chest conditions of patients. Methods: The proposed system consists of Variable stiffness mechanisms (VSMs) and Variable damper (VD) utilizing stretching silicone bands and dashpot dampers with controllable valves to modulate stiffness and damping, respectively. Cyclic loading was applied with a robot manipulator to the system. Compression force and displacement were measured and analyzed to evaluate the system’s mechanical response. Long-term stability of the system was also validated. Results: A non-linear response of the human chest under compression is realized through this design. Test results indicated non-linear force-displacement curves with hysteresis, similar to those observed in the chest of patients. Controlling the VSM and VD allowed for intentional changes in the slope and area of curves that are related to stiffness and damping, respectively. Stiffness and damping of the system were computed using performance test results. The stiffness ranged from 5.34 N/mm to 13.59 N/mm and the damping ranges from 0.127 N $cdot $ s/mm to 0.511 N $cdot $ s/mm. These properties cover a significant portion of the reported mechanical properties of the human chests. The system demonstrated satisfactory stability even when it was subjected to maximum stiffness conditions of the long-term compression test. Conclusion: The system is capable of emulating the mechanical properties and behavior of the human chests, thereby enhancing the CPR training experience.
目的：本研究介绍了一种新型系统，该系统可模拟人体胸部的各种机械特性，通过反映患者胸部的真实情况来增强心肺复苏训练的体验。方法：拟议的系统由可变刚度机构（VSM）和可变阻尼器（VD）组成，分别利用拉伸硅胶带和带可控阀门的仪表盘阻尼器来调节刚度和阻尼。使用机器人机械手对系统施加循环加载。对压缩力和位移进行测量和分析，以评估系统的机械响应。同时还验证了系统的长期稳定性。结果该设计实现了人体胸部在压缩下的非线性响应。测试结果表明，非线性力-位移曲线具有滞后性，与在患者胸部观察到的曲线相似。通过控制 VSM 和 VD，可以有意改变曲线的斜率和面积，这分别与刚度和阻尼有关。系统的刚度和阻尼是根据性能测试结果计算得出的。刚度范围为 5.34 N/mm 至 13.59 N/mm，阻尼范围为 0.127 N $cdot $ s/mm 至 0.511 N $cdot $ s/mm。这些特性涵盖了所报道的人体胸部机械特性的很大一部分。即使在长期压缩试验的最大刚度条件下，该系统也表现出令人满意的稳定性。结论该系统能够模拟人体胸腔的机械特性和行为，从而增强心肺复苏训练体验。
{"title":"Variable Stiffness and Damping Mechanism for CPR Manikin to Simulate Mechanical Properties of Human Chest","authors":"Hyungsoo Lim;Dong Ah Shin;Jaehoon Sim;Jaeheung Park;Taegyun Kim;Kyung Su Kim;Gil Joon Suh;Jung Chan Lee","doi":"10.1109/JTEHM.2024.3429422","DOIUrl":"10.1109/JTEHM.2024.3429422","url":null,"abstract":"Objective: This study introduces a novel system that can simulate diverse mechanical properties of the human chest to enhance the experience of CPR training by reflecting realistic chest conditions of patients. Methods: The proposed system consists of Variable stiffness mechanisms (VSMs) and Variable damper (VD) utilizing stretching silicone bands and dashpot dampers with controllable valves to modulate stiffness and damping, respectively. Cyclic loading was applied with a robot manipulator to the system. Compression force and displacement were measured and analyzed to evaluate the system’s mechanical response. Long-term stability of the system was also validated. Results: A non-linear response of the human chest under compression is realized through this design. Test results indicated non-linear force-displacement curves with hysteresis, similar to those observed in the chest of patients. Controlling the VSM and VD allowed for intentional changes in the slope and area of curves that are related to stiffness and damping, respectively. Stiffness and damping of the system were computed using performance test results. The stiffness ranged from 5.34 N/mm to 13.59 N/mm and the damping ranges from 0.127 N\u0000<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>\u0000 s/mm to 0.511 N\u0000<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>\u0000 s/mm. These properties cover a significant portion of the reported mechanical properties of the human chests. The system demonstrated satisfactory stability even when it was subjected to maximum stiffness conditions of the long-term compression test. Conclusion: The system is capable of emulating the mechanical properties and behavior of the human chests, thereby enhancing the CPR training experience.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"542-549"},"PeriodicalIF":3.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The equivalent electrical circuit approach is explored to improve a bioimpedance-based transducer for measuring the bioavailability of synthetic insulin already presented in previous studies. In particular, the electrical parameter most sensitive to the variation of insulin amount injected was identified. Eggplants were used to emulate human electrical behavior under a quasi-static assumption guaranteed by a very low measurement time compared to the estimated insulin absorption time. Measurements were conducted with the EVAL-AD5940BIOZ by applying a sinusoidal voltage signal with an amplitude of 100 mV and acquiring impedance spectra in the range [1–100] kHz. 14 units of insulin were gradually administered using a Lilly’s Insulin Pen having a 0.4 cm long needle. Modified Hayden’s model was adopted as a reference circuit and the electrical component modeling the extracellular fluids was found to be the most insulin-sensitive parameter. The trnasducer achieves a state-of-the-art sensitivity of 225.90 ml1. An improvement of 223 % in sensitivity, 44 % in deterministic error, 7 % in nonlinearity, and 42 % in reproducibility was achieved compared to previous experimental studies. The clinical impact of the transducer was evaluated by projecting its impact on a Smart Insulin Pen for real-time measurement of insulin bioavailability. The wide gain in sensitivity of the bioimpedance-based transducer results in a significant reduction of the uncertainty of the Smart Insulin Pen. Considering the same improvement in in-vivo applications, the uncertainty of the Smart Insulin Pen is decreased from $4.2~mu $ l to $1.3~mu $ l.Clinical and Translational Impact Statement: A Smart Insulin Pen based on impedance spectroscopy and equivalent electrical circuit approach could be an effective solution for the non-invasive and real-time measurement of synthetic insulin uptake after subcutaneous administration.
{"title":"Equivalent Electrical Circuit Approach to Enhance a Transducer for Insulin Bioavailability Assessment","authors":"Francesca Mancino;Hanen Nouri;Nicola Moccaldi;Pasquale Arpaia;Olfa Kanoun","doi":"10.1109/JTEHM.2024.3425269","DOIUrl":"10.1109/JTEHM.2024.3425269","url":null,"abstract":"The equivalent electrical circuit approach is explored to improve a bioimpedance-based transducer for measuring the bioavailability of synthetic insulin already presented in previous studies. In particular, the electrical parameter most sensitive to the variation of insulin amount injected was identified. Eggplants were used to emulate human electrical behavior under a quasi-static assumption guaranteed by a very low measurement time compared to the estimated insulin absorption time. Measurements were conducted with the EVAL-AD5940BIOZ by applying a sinusoidal voltage signal with an amplitude of 100 mV and acquiring impedance spectra in the range [1–100] kHz. 14 units of insulin were gradually administered using a Lilly’s Insulin Pen having a 0.4 cm long needle. Modified Hayden’s model was adopted as a reference circuit and the electrical component modeling the extracellular fluids was found to be the most insulin-sensitive parameter. The trnasducer achieves a state-of-the-art sensitivity of 225.90 ml1. An improvement of 223 % in sensitivity, 44 % in deterministic error, 7 % in nonlinearity, and 42 % in reproducibility was achieved compared to previous experimental studies. The clinical impact of the transducer was evaluated by projecting its impact on a Smart Insulin Pen for real-time measurement of insulin bioavailability. The wide gain in sensitivity of the bioimpedance-based transducer results in a significant reduction of the uncertainty of the Smart Insulin Pen. Considering the same improvement in in-vivo applications, the uncertainty of the Smart Insulin Pen is decreased from \u0000<inline-formula> <tex-math>$4.2~mu $ </tex-math></inline-formula>\u0000l to \u0000<inline-formula> <tex-math>$1.3~mu $ </tex-math></inline-formula>\u0000l.Clinical and Translational Impact Statement: A Smart Insulin Pen based on impedance spectroscopy and equivalent electrical circuit approach could be an effective solution for the non-invasive and real-time measurement of synthetic insulin uptake after subcutaneous administration.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"533-541"},"PeriodicalIF":3.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Slow and deep breathing (SDB) is a relaxation technique that can increase vagal activity. Respiratory sinus arrhythmia (RSA) serves as an index of vagal function usually quantified by the high-frequency power of heart rate variability (HRV). However, the low breathing rate during SDB results in deviations when estimating RSA by HRV. Besides, the impact of the inspiration-expiration (I: E) ratio and guidelines ways (fixed breathing rate or intelligent guidance) on SDB is not yet clear. In our study, 30 healthy people (mean age = 26.5 years, 17 females) participated in three SDB modes, including 6 breaths per minute (bpm) with an I:E ratio of 1:1/ 1:2, and intelligent guidance mode (I:E ratio of 1:2 with guiding to gradually lower breathing rate to 6 bpm). Parameters derived from HRV, multimodal coupling analysis (MMCA), Poincaré plot, and detrended fluctuation analysis were introduced to examine the effects of SDB exercises. Besides, multiple machine learning methods were applied to classify breathing patterns (spontaneous breathing vs. SDB) after feature selection by max-relevance and min-redundancy. All vagal-activity markers, especially MMCA-derived RSA, statistically increased during SDB. Among all SDB modes, breathing at 6 bpm with a 1:1 I:E ratio activated the vagal function the most statistically, while the intelligent guidance mode had more indicators that still significantly increased after training, including SDRR and MMCA-derived RSA, etc. About the classification of breathing patterns, the Naive Bayes classifier has the highest accuracy (92.2%) with input features including LFn, CPercent, pNN50, $alpha 2$ , SDRatio, $alpha 1$ , and LF. Our study proposed a system that can be applied to medical devices for automatic SDB identification and real-time feedback on the training effect. We demonstrated that breathing at 6 bpm with an I:E ratio of 1:1 performed best during the training phase, while intelligent guidance mode had a more long-lasting effect.
{"title":"Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation","authors":"Deshan Ma;Conghui Li;Wenbin Shi;Yong Fan;Hong Liang;Lixuan Li;Zhengbo Zhang;Chien-Hung Yeh","doi":"10.1109/JTEHM.2024.3419805","DOIUrl":"10.1109/JTEHM.2024.3419805","url":null,"abstract":"Slow and deep breathing (SDB) is a relaxation technique that can increase vagal activity. Respiratory sinus arrhythmia (RSA) serves as an index of vagal function usually quantified by the high-frequency power of heart rate variability (HRV). However, the low breathing rate during SDB results in deviations when estimating RSA by HRV. Besides, the impact of the inspiration-expiration (I: E) ratio and guidelines ways (fixed breathing rate or intelligent guidance) on SDB is not yet clear. In our study, 30 healthy people (mean age = 26.5 years, 17 females) participated in three SDB modes, including 6 breaths per minute (bpm) with an I:E ratio of 1:1/ 1:2, and intelligent guidance mode (I:E ratio of 1:2 with guiding to gradually lower breathing rate to 6 bpm). Parameters derived from HRV, multimodal coupling analysis (MMCA), Poincaré plot, and detrended fluctuation analysis were introduced to examine the effects of SDB exercises. Besides, multiple machine learning methods were applied to classify breathing patterns (spontaneous breathing vs. SDB) after feature selection by max-relevance and min-redundancy. All vagal-activity markers, especially MMCA-derived RSA, statistically increased during SDB. Among all SDB modes, breathing at 6 bpm with a 1:1 I:E ratio activated the vagal function the most statistically, while the intelligent guidance mode had more indicators that still significantly increased after training, including SDRR and MMCA-derived RSA, etc. About the classification of breathing patterns, the Naive Bayes classifier has the highest accuracy (92.2%) with input features including LFn, CPercent, pNN50, \u0000<inline-formula> <tex-math>$alpha 2$ </tex-math></inline-formula>\u0000, SDRatio, \u0000<inline-formula> <tex-math>$alpha 1$ </tex-math></inline-formula>\u0000, and LF. Our study proposed a system that can be applied to medical devices for automatic SDB identification and real-time feedback on the training effect. We demonstrated that breathing at 6 bpm with an I:E ratio of 1:1 performed best during the training phase, while intelligent guidance mode had a more long-lasting effect.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"520-532"},"PeriodicalIF":3.7,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10574824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-24DOI: 10.1109/JTEHM.2024.3418345
John Harvill;Moitreya Chatterjee;Shaveta Khosla;Mustafa Alam;Narendra Ahuja;Mark Hasegawa-Johnson;David Chestek;David G. Beiser
Given the recent COVID-19 pandemic, there has been a push in the medical community for reliable, remote medical care. The ubiquity of smartphone devices has brought about much interest in the estimation of patient vital signs via an audio or video signal.Objective: In this paper, our objective is to estimate and compare respiratory rates from video, from audio, and jointly from video and audio for emergency department patients.Methods and procedures: For video, we use signal processing techniques, whereas for audio, we compare respiration rate estimates obtained using signal processing methods and learning-based methods due to the public availability of a large annotated audio corpus of breathing sounds.Results: On our collected audio-video corpus, we achieve the best Mean Absolute Error (MAE) of 2.53 when using video features. For the publicly available respiratory rate corpus, we achieve MAE of 1.63 when using signal processing methods.Conclusion: Based on the experimental results from our clinical data, we draw the conclusion that the video modality yields more accurate estimates when compared to the audio modality.Clinical impact: Accurate, contactless estimation of vital signs using video or audio is significant, because it can be performed remotely. Additionally, it is contactless and does not require extra measurement equipment.
{"title":"Multimodal Respiratory Rate Estimation From Audio and Video in Emergency Department Patients","authors":"John Harvill;Moitreya Chatterjee;Shaveta Khosla;Mustafa Alam;Narendra Ahuja;Mark Hasegawa-Johnson;David Chestek;David G. Beiser","doi":"10.1109/JTEHM.2024.3418345","DOIUrl":"10.1109/JTEHM.2024.3418345","url":null,"abstract":"Given the recent COVID-19 pandemic, there has been a push in the medical community for reliable, remote medical care. The ubiquity of smartphone devices has brought about much interest in the estimation of patient vital signs via an audio or video signal.Objective: In this paper, our objective is to estimate and compare respiratory rates from video, from audio, and jointly from video and audio for emergency department patients.Methods and procedures: For video, we use signal processing techniques, whereas for audio, we compare respiration rate estimates obtained using signal processing methods and learning-based methods due to the public availability of a large annotated audio corpus of breathing sounds.Results: On our collected audio-video corpus, we achieve the best Mean Absolute Error (MAE) of 2.53 when using video features. For the publicly available respiratory rate corpus, we achieve MAE of 1.63 when using signal processing methods.Conclusion: Based on the experimental results from our clinical data, we draw the conclusion that the video modality yields more accurate estimates when compared to the audio modality.Clinical impact: Accurate, contactless estimation of vital signs using video or audio is significant, because it can be performed remotely. Additionally, it is contactless and does not require extra measurement equipment.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"552-560"},"PeriodicalIF":4.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1109/JTEHM.2024.3415412
Yohanna MejiaCruz;Juan M. Caicedo;Zhaoshuo Jiang;Jean M. Franco
Objective: This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment.Methods and Procedures: A multilevel probabilistic model was developed to estimate cadence and walking speed through the analysis of floor vibrations induced by walking. The model addresses challenges related to missing or incomplete information in the floor acceleration signals. Following the Bayesian Analysis Reporting Guidelines (BARG) for reproducibility, the model was evaluated through twenty-seven walking experiments, capturing floor vibration and data from Ambulatory Parkinson’s Disease Monitoring (APDM) wearable sensors. The model was tested in a real-time implementation where ten individuals were recorded walking at their own selected pace.Results: Using a rigorous combined decision criteria of 95% high posterior density (HPD) and the Range of Practical Equivalence (ROPE) following BARG, the results demonstrate satisfactory alignment between estimations and target values for practical purposes. Notably, with over 90% of the 95% HPD falling within the region of practical equivalence, there is a solid basis for accepting the estimations as probabilistically aligned with the estimations using the APDM sensors and video recordings.Conclusion: This research validates the probabilistic multilevel model in estimating cadence and walking speed by analyzing floor vibrations, demonstrating its satisfactory comparability with established technologies such as APDM sensors and video recordings. The close alignment between the estimations and target values emphasizes the approach’s efficacy. The proposed model effectively tackles prevalent challenges associated with missing or incomplete data in real-world scenarios, enhancing the accuracy of gait parameter estimations derived from floor vibrations.Clinical impact: Extracting gait parameters from floor vibrations could provide a non-intrusive and continuous means of monitoring an individual’s gait, offering valuable insights into mobility and potential indicators of neurological conditions. The implications of this research extend to the development of advanced gait analysis tools, offering new perspectives on assessing and understanding walking patterns for improved diagnostics and personalized healthcare.Clinical and Translational Impact Statement: This manuscript introduces an innovative approach for unattended gait assessments with potentially significant implications for clinical decision-making. By utilizing floor vibrations to estimate cadence and walking speed, the technology can provide clinicians with valuable insights into their patients’ mobility and functional abilities in real-life settings. The strategic installation of accelerometers beneath the flooring of homes or care facilities allows for uninterrupted daily activities
{"title":"Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations","authors":"Yohanna MejiaCruz;Juan M. Caicedo;Zhaoshuo Jiang;Jean M. Franco","doi":"10.1109/JTEHM.2024.3415412","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3415412","url":null,"abstract":"Objective: This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment.Methods and Procedures: A multilevel probabilistic model was developed to estimate cadence and walking speed through the analysis of floor vibrations induced by walking. The model addresses challenges related to missing or incomplete information in the floor acceleration signals. Following the Bayesian Analysis Reporting Guidelines (BARG) for reproducibility, the model was evaluated through twenty-seven walking experiments, capturing floor vibration and data from Ambulatory Parkinson’s Disease Monitoring (APDM) wearable sensors. The model was tested in a real-time implementation where ten individuals were recorded walking at their own selected pace.Results: Using a rigorous combined decision criteria of 95% high posterior density (HPD) and the Range of Practical Equivalence (ROPE) following BARG, the results demonstrate satisfactory alignment between estimations and target values for practical purposes. Notably, with over 90% of the 95% HPD falling within the region of practical equivalence, there is a solid basis for accepting the estimations as probabilistically aligned with the estimations using the APDM sensors and video recordings.Conclusion: This research validates the probabilistic multilevel model in estimating cadence and walking speed by analyzing floor vibrations, demonstrating its satisfactory comparability with established technologies such as APDM sensors and video recordings. The close alignment between the estimations and target values emphasizes the approach’s efficacy. The proposed model effectively tackles prevalent challenges associated with missing or incomplete data in real-world scenarios, enhancing the accuracy of gait parameter estimations derived from floor vibrations.Clinical impact: Extracting gait parameters from floor vibrations could provide a non-intrusive and continuous means of monitoring an individual’s gait, offering valuable insights into mobility and potential indicators of neurological conditions. The implications of this research extend to the development of advanced gait analysis tools, offering new perspectives on assessing and understanding walking patterns for improved diagnostics and personalized healthcare.Clinical and Translational Impact Statement: This manuscript introduces an innovative approach for unattended gait assessments with potentially significant implications for clinical decision-making. By utilizing floor vibrations to estimate cadence and walking speed, the technology can provide clinicians with valuable insights into their patients’ mobility and functional abilities in real-life settings. The strategic installation of accelerometers beneath the flooring of homes or care facilities allows for uninterrupted daily activities","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"508-519"},"PeriodicalIF":3.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1109/JTEHM.2024.3388852
Ghena Hammour;Harry Davies;Giuseppe Atzori;Ciro Della Monica;Kiran K. G. Ravindran;Victoria Revell;Derk-Jan Dijk;Danilo P. Mandic
Objective: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG.Methods and procedures: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. Results: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen’s kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process.Conclusion: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques.Clinical impact: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.
{"title":"From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People","authors":"Ghena Hammour;Harry Davies;Giuseppe Atzori;Ciro Della Monica;Kiran K. G. Ravindran;Victoria Revell;Derk-Jan Dijk;Danilo P. Mandic","doi":"10.1109/JTEHM.2024.3388852","DOIUrl":"10.1109/JTEHM.2024.3388852","url":null,"abstract":"Objective: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG.Methods and procedures: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. Results: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen’s kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process.Conclusion: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques.Clinical impact: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"448-456"},"PeriodicalIF":3.4,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1109/JTEHM.2024.3390589
Phuong Truong;Erin Walsh;Vanessa P. Scott;Michelle Leff;Alice Chen;James Friend
Objective: Identify infants with abnormal suckling behavior from simple non-nutritive suckling devices.Background: While it is well known breastfeeding is beneficial to the health of both mothers and infants, breastfeeding ceases in 75 percent of mother-child dyads by 6 months. The current standard of care lacks objective measurements to screen infant suckling abnormalities within the first few days of life, a critical time to establish milk supply and successful breastfeeding practices.Materials and Methods: A non-nutritive suckling vacuum measurement system, previously developed by the authors, is used to gather data from 91 healthy full-term infants under thirty days old. Non-nutritive suckling was recorded for a duration of sixty seconds. We establish normative data for the mean suck vacuum, maximum suck vacuum, suckling frequency, burst duration, sucks per burst, and vacuum signal shape. We then apply computational methods (Mahalanobis distance, KNN) to detect anomalies in the data to identify infants with abnormal suckling. We finally provide case studies of healthy newborn infants and infants diagnosed with ankyloglossia.Results: In a series of case evaluations, we demonstrate the ability to detect abnormal suckling behavior using statistical analysis and machine learning. We evaluate cases of ankyloglossia to determine how oral dysfunction and surgical interventions affect non-nutritive suckling measurements.Conclusions: Statistical analysis (Mahalanobis Distance) and machine learning [K nearest neighbor (KNN)] can be viable approaches to rapidly interpret infant suckling measurements. Particularly in practices using the digital suck assessment with a gloved finger, it can provide a more objective, early stage screening method to identify abnormal infant suckling vacuum. This approach for identifying those at risk for breastfeeding complications is crucial to complement complex emerging clinical evaluation technology.Clinical Impact: By analyzing non-nutritive suckling using computational methods, we demonstrate the ability to detect abnormal and normal behavior in infant suckling that can inform breastfeeding intervention pathways in clinic.Clinical and Translational Impact Statement: The work serves to shed light on the lack of consensus for determining appropriate intervention pathways for infant oral dysfunction. We demonstrate using statistical analysis and machine learning that normal and abnormal infant suckling can be identified and used in determining if surgical intervention is a necessary solution to resolve infant feeding difficulties.
{"title":"Application of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior","authors":"Phuong Truong;Erin Walsh;Vanessa P. Scott;Michelle Leff;Alice Chen;James Friend","doi":"10.1109/JTEHM.2024.3390589","DOIUrl":"10.1109/JTEHM.2024.3390589","url":null,"abstract":"Objective: Identify infants with abnormal suckling behavior from simple non-nutritive suckling devices.Background: While it is well known breastfeeding is beneficial to the health of both mothers and infants, breastfeeding ceases in 75 percent of mother-child dyads by 6 months. The current standard of care lacks objective measurements to screen infant suckling abnormalities within the first few days of life, a critical time to establish milk supply and successful breastfeeding practices.Materials and Methods: A non-nutritive suckling vacuum measurement system, previously developed by the authors, is used to gather data from 91 healthy full-term infants under thirty days old. Non-nutritive suckling was recorded for a duration of sixty seconds. We establish normative data for the mean suck vacuum, maximum suck vacuum, suckling frequency, burst duration, sucks per burst, and vacuum signal shape. We then apply computational methods (Mahalanobis distance, KNN) to detect anomalies in the data to identify infants with abnormal suckling. We finally provide case studies of healthy newborn infants and infants diagnosed with ankyloglossia.Results: In a series of case evaluations, we demonstrate the ability to detect abnormal suckling behavior using statistical analysis and machine learning. We evaluate cases of ankyloglossia to determine how oral dysfunction and surgical interventions affect non-nutritive suckling measurements.Conclusions: Statistical analysis (Mahalanobis Distance) and machine learning [K nearest neighbor (KNN)] can be viable approaches to rapidly interpret infant suckling measurements. Particularly in practices using the digital suck assessment with a gloved finger, it can provide a more objective, early stage screening method to identify abnormal infant suckling vacuum. This approach for identifying those at risk for breastfeeding complications is crucial to complement complex emerging clinical evaluation technology.Clinical Impact: By analyzing non-nutritive suckling using computational methods, we demonstrate the ability to detect abnormal and normal behavior in infant suckling that can inform breastfeeding intervention pathways in clinic.Clinical and Translational Impact Statement: The work serves to shed light on the lack of consensus for determining appropriate intervention pathways for infant oral dysfunction. We demonstrate using statistical analysis and machine learning that normal and abnormal infant suckling can be identified and used in determining if surgical intervention is a necessary solution to resolve infant feeding difficulties.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"435-447"},"PeriodicalIF":3.4,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.1109/JTEHM.2024.3388561
Morgan Connaughton;Mahsa Dabagh
Mechanical force exerted on cancer cells by their microenvironment have been reported to drive cells toward invasive phenotypes by altering cells’ motility, proliferation, and apoptosis. These mechanical forces include compressive, tensile, hydrostatic, and shear forces. The importance of forces is then hypothesized to be an alteration of cancer cells’ and their microenvironment’s biophysical properties as the indicator of a tumor’s malignancy state. Our objective is to investigate and quantify the correlation between a tumor’s malignancy state and forces experienced by the cancer cells and components of the microenvironment. In this study, we have developed a multicomponent, three-dimensional model of tumor tissue consisting of a cancer cell surrounded by fibroblasts and extracellular matrix (ECM). Our results on three different organs including breast, kidney, and pancreas show that: A) the stresses within tumor tissue are impacted by the organ specific ECM’s biophysical properties, B) more invasive cancer cells experience higher stresses, C) in pancreas which has a softer ECM (Young modulus of 1.0 kPa) and stiffer cancer cells (Young modulus of 2.4 kPa and 1.7 kPa) than breast and kidney, cancer cells experienced significantly higher stresses, D) cancer cells in contact with ECM experienced higher stresses compared to cells surrounded by fibroblasts but the area of tumor stroma experiencing high stresses has a maximum length of $40 ~mu text{m}$ when the cancer cell is surrounded by fibroblasts and $12 ~mu text{m}$ for when the cancer cell is in vicinity of ECM. This study serves as an important first step in understanding of how the stresses experienced by cancer cells, fibroblasts, and ECM are associated with malignancy states of cancer cells in different organs. The quantification of forces exerted on cancer cells by different organ-specific ECM and at different stages of malignancy will help, first to develop theranostic strategies, second to predict accurately which tumors will become highly malignant, and third to establish accurate criteria controlling the progression of cancer cells malignancy. Furthermore, our in silico model of tumor tissue can yield critical, useful information for guiding ex vivo or in vitro experiments, narrowing down variables to be investigated, understanding what factors could be impacting cancer treatments or even biomarkers to be looking for.
{"title":"Modeling Physical Forces Experienced by Cancer and Stromal Cells Within Different Organ-Specific Tumor Tissue","authors":"Morgan Connaughton;Mahsa Dabagh","doi":"10.1109/JTEHM.2024.3388561","DOIUrl":"10.1109/JTEHM.2024.3388561","url":null,"abstract":"Mechanical force exerted on cancer cells by their microenvironment have been reported to drive cells toward invasive phenotypes by altering cells’ motility, proliferation, and apoptosis. These mechanical forces include compressive, tensile, hydrostatic, and shear forces. The importance of forces is then hypothesized to be an alteration of cancer cells’ and their microenvironment’s biophysical properties as the indicator of a tumor’s malignancy state. Our objective is to investigate and quantify the correlation between a tumor’s malignancy state and forces experienced by the cancer cells and components of the microenvironment. In this study, we have developed a multicomponent, three-dimensional model of tumor tissue consisting of a cancer cell surrounded by fibroblasts and extracellular matrix (ECM). Our results on three different organs including breast, kidney, and pancreas show that: A) the stresses within tumor tissue are impacted by the organ specific ECM’s biophysical properties, B) more invasive cancer cells experience higher stresses, C) in pancreas which has a softer ECM (Young modulus of 1.0 kPa) and stiffer cancer cells (Young modulus of 2.4 kPa and 1.7 kPa) than breast and kidney, cancer cells experienced significantly higher stresses, D) cancer cells in contact with ECM experienced higher stresses compared to cells surrounded by fibroblasts but the area of tumor stroma experiencing high stresses has a maximum length of \u0000<inline-formula> <tex-math>$40 ~mu text{m}$ </tex-math></inline-formula>\u0000 when the cancer cell is surrounded by fibroblasts and \u0000<inline-formula> <tex-math>$12 ~mu text{m}$ </tex-math></inline-formula>\u0000 for when the cancer cell is in vicinity of ECM. This study serves as an important first step in understanding of how the stresses experienced by cancer cells, fibroblasts, and ECM are associated with malignancy states of cancer cells in different organs. The quantification of forces exerted on cancer cells by different organ-specific ECM and at different stages of malignancy will help, first to develop theranostic strategies, second to predict accurately which tumors will become highly malignant, and third to establish accurate criteria controlling the progression of cancer cells malignancy. Furthermore, our in silico model of tumor tissue can yield critical, useful information for guiding ex vivo or in vitro experiments, narrowing down variables to be investigated, understanding what factors could be impacting cancer treatments or even biomarkers to be looking for.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"413-434"},"PeriodicalIF":3.4,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10499240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140579954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-31DOI: 10.1109/JTEHM.2024.3407951
Sofia Basha;Mohammad Khorasani;Nihal Abdurahiman;Jhasketan Padhan;Victor Baez;Abdulla Al-Ansari;Panagiotis Tsiamyrtzis;Aaron T. Becker;Nikhil V. Navkar
Objective: Variable-view rigid scopes offer advantages compared to traditional angled laparoscopes for examining a diagnostic site. However, altering the scope’s view requires a high level of dexterity and understanding of spatial orientation. This requires an intuitive mechanism to allow an operator to easily understand the anatomical surroundings and smoothly adjust the scope’s focus during diagnosis. To address this challenge, the objective of this work is to develop a mechanized arm that assists in visualization using variable-view rigid scopes during diagnostic procedures.Methods: A system with a mechanized arm to maneuver a variable-view rigid scope (EndoCAMeleon - Karl Storz) was developed. A user study was conducted to assess the ability of the proposed mechanized arm for diagnosis in a preclinical navigation task and a simulated cystoscopy procedure.Results: The mechanized arm performed significantly better than direct maneuvering of the rigid scope. In the preclinical navigation task, it reduced the percentage of time the scope’s focus shifted outside a predefined track. Similarly, for simulated cystoscopy procedure, it reduced the duration and the perceived workload.Conclusion: The proposed mechanized arm enhances the operator’s ability to accurately maneuver a variable-view rigid scope and reduces the effort in performing diagnostic procedures.Clinical and Translational Impact Statement: The preclinical research introduces a mechanized arm to intuitively maneuver a variable-view rigid scope during diagnostic procedures, while minimizing the mental and physical workload to the operator.
目的：与传统的倾斜腹腔镜相比，可变视角刚性腹腔镜在检查诊断部位方面具有优势。然而，改变瞄准镜的视角需要高度的灵活性和对空间方位的理解。这就需要一种直观的机制，让操作员能够轻松了解周围的解剖环境，并在诊断过程中顺利调整瞄准镜的焦点。为了应对这一挑战，这项工作的目标是开发一种机械化手臂，在诊断过程中使用可变视角刚性显微镜辅助观察：方法：开发了一种带有机械臂的系统，用于操纵可变视角硬镜（EndoCAMeleon - Karl Storz）。结果：机械化手臂在临床前导航任务和模拟膀胱镜检查过程中的表现明显优于机械化手臂：结果：机械化手臂的表现明显优于直接操纵硬镜。在临床前导航任务中，它减少了瞄准镜焦点偏离预定轨道的时间百分比。同样，在模拟膀胱镜检查过程中，机械臂缩短了持续时间，减轻了感知工作量：结论：拟议的机械化手臂提高了操作员准确操纵可变视角刚性镜的能力，并减少了执行诊断程序的工作量：临床前研究引入了一种机械化手臂，可在诊断过程中直观地操纵可变视角硬镜，同时最大限度地减轻操作者的脑力和体力负担。
{"title":"An Actuated Variable-View Rigid Scope System to Assist Visualization in Diagnostic Procedures","authors":"Sofia Basha;Mohammad Khorasani;Nihal Abdurahiman;Jhasketan Padhan;Victor Baez;Abdulla Al-Ansari;Panagiotis Tsiamyrtzis;Aaron T. Becker;Nikhil V. Navkar","doi":"10.1109/JTEHM.2024.3407951","DOIUrl":"10.1109/JTEHM.2024.3407951","url":null,"abstract":"Objective: Variable-view rigid scopes offer advantages compared to traditional angled laparoscopes for examining a diagnostic site. However, altering the scope’s view requires a high level of dexterity and understanding of spatial orientation. This requires an intuitive mechanism to allow an operator to easily understand the anatomical surroundings and smoothly adjust the scope’s focus during diagnosis. To address this challenge, the objective of this work is to develop a mechanized arm that assists in visualization using variable-view rigid scopes during diagnostic procedures.Methods: A system with a mechanized arm to maneuver a variable-view rigid scope (EndoCAMeleon - Karl Storz) was developed. A user study was conducted to assess the ability of the proposed mechanized arm for diagnosis in a preclinical navigation task and a simulated cystoscopy procedure.Results: The mechanized arm performed significantly better than direct maneuvering of the rigid scope. In the preclinical navigation task, it reduced the percentage of time the scope’s focus shifted outside a predefined track. Similarly, for simulated cystoscopy procedure, it reduced the duration and the perceived workload.Conclusion: The proposed mechanized arm enhances the operator’s ability to accurately maneuver a variable-view rigid scope and reduces the effort in performing diagnostic procedures.Clinical and Translational Impact Statement: The preclinical research introduces a mechanized arm to intuitively maneuver a variable-view rigid scope during diagnostic procedures, while minimizing the mental and physical workload to the operator.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"499-507"},"PeriodicalIF":3.4,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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