Pub Date : 2025-07-01DOI: 10.1016/j.bbe.2025.08.003
Piotr Foltynski , Karolina Kruszewska , Arkadiusz Krakowiecki , Bozena Czarkowska-Paczek , Piotr Ladyzynski
Recognizing an infected wound based solely on a photograph can be a challenge and the aim of this work was to develop a machine learning model that would enable that. We selected 899 wound photographs taken at PODOS Wound Care Clinic (Warsaw, Poland). There were 445 photographs showing uninfected wounds, whereas 454 photographs showed infected wounds with positive microbiological test and antibiotic treatment. A test set was created by randomly selecting 82 photographs representing 42 uninfected and 40 infected wounds. From the remaining photographs, 154 were randomly selected for the validation set, and the remaining 663 formed the training set. Initially we used five pretrained YOLO models from generation 8 and five from generation 11. The 8th generation models performed better than 11th generation models and were then compared with the results of 6 experts and 6 nursing students. The post-hoc analysis revealed that AI models outperformed both specialists and students in terms of mean averaged precision (mAP), accuracy and F1 score, while the results of specialists and students did not differ significantly. For specialists, the medians of mAP, F1 score, and accuracy were 74.1 %, 76.4 %, and 74.4 %, respectively. For Students the medians were 68.4 %, 59.4 %, and 67.7 %, respectively; and for AI models the medians were 92.7 %, 92.9 %, and 92.7 %, respectively. The highest accuracy of 95.1 % of YOLOv8n model was significantly higher than the best specialist’s result of 84.1 %. These results suggest that artificial intelligence can significantly help caregivers recognize wound infection, so they can take appropriate action more quickly.
{"title":"Artificial intelligence models for wound infection recognition and their comparison with human results","authors":"Piotr Foltynski , Karolina Kruszewska , Arkadiusz Krakowiecki , Bozena Czarkowska-Paczek , Piotr Ladyzynski","doi":"10.1016/j.bbe.2025.08.003","DOIUrl":"10.1016/j.bbe.2025.08.003","url":null,"abstract":"<div><div>Recognizing an infected wound based solely on a photograph can be a challenge and the aim of this work was to develop a machine learning model that would enable that. We selected 899 wound photographs taken at PODOS Wound Care Clinic (Warsaw, Poland). There were 445 photographs showing uninfected wounds, whereas 454 photographs showed infected wounds with positive microbiological test and antibiotic treatment. A test set was created by randomly selecting 82 photographs representing 42 uninfected and 40 infected wounds. From the remaining photographs, 154 were randomly selected for the validation set, and the remaining 663 formed the training set. Initially we used five pretrained YOLO models from generation 8 and five from generation 11. The 8th generation models performed better than 11th generation models and were then compared with the results of 6 experts and 6 nursing students. The post-hoc analysis revealed that AI models outperformed both specialists and students in terms of mean averaged precision (mAP), accuracy and F1 score, while the results of specialists and students did not differ significantly. For specialists, the medians of mAP, F1 score, and accuracy were 74.1 %, 76.4 %, and 74.4 %, respectively. For Students the medians were 68.4 %, 59.4 %, and 67.7 %, respectively; and for AI models the medians were 92.7 %, 92.9 %, and 92.7 %, respectively. The highest accuracy of 95.1 % of YOLOv8n model was significantly higher than the best specialist’s result of 84.1 %. These results suggest that artificial intelligence can significantly help caregivers recognize wound infection, so they can take appropriate action more quickly.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 572-579"},"PeriodicalIF":6.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.bbe.2025.07.004
Luca Pietrosanti , Martina Patera , Antonio Suppa , Giovanni Costantini , Nicola Arangino , Franco Giannini , Giovanni Saggio
Hand functions are vital for performing daily activities, ensuring independence, and maintaining quality of life. In Parkinson’s disease (PD), impaired hand function affects fine motor skills, dexterity, and coordination, leading to difficulties in self-care, communication, and work-related tasks. As such, correct hand function assessment in PD is among the crucial aspects in evaluating motor impairment, in guiding treatment and tracking disease progression. Here, we report objective results obtained in assessing hand (dys)functionalities using an on-the-shelves fingerless sensory glove, named MANUS Quantum Metaglove, capable of sensing the variations of an electromagnetic field (EMF) sourced on the dorsal part of the hand and revealed by EMF coils at the fingers tips. A total of 65 people (35 PD patients and 30 healthy subjects for reference) were asked to perform standard motor tasks, and both most affected and least affected hands were assessed for opening-closing, grasping and pronation-supination movements. Differing from the generally adopted spatiotemporal analysis, taking a cue from non-linear theory adopted in electronics, we focused on spectral characteristics of the measured signals, specifically examining harmonic content and related harmonic distortions. As a result, we report how the adopted sensory glove, ensemble with spectral analysis, can be able to consistently assess hand motor (in)abilities in PD subjects and healthy subjects. In fact according to our results, PD patients significatively performed with hand motion signals affected by harmonic distortions, which revealed that the greater the complexity of the motor task, the greater the spread of the signal across harmonic frequencies, whilst healthy subjects perform with signals mostly around the fundamental frequency, as a marker of movement smoothness.
{"title":"Relevance of harmonic content findings of hand motor (dys)functionalities in Parkinson’s disease revealed by means of a sensory glove","authors":"Luca Pietrosanti , Martina Patera , Antonio Suppa , Giovanni Costantini , Nicola Arangino , Franco Giannini , Giovanni Saggio","doi":"10.1016/j.bbe.2025.07.004","DOIUrl":"10.1016/j.bbe.2025.07.004","url":null,"abstract":"<div><div>Hand functions are vital for performing daily activities, ensuring independence, and maintaining quality of life. In Parkinson’s disease (PD), impaired hand function affects fine motor skills, dexterity, and coordination, leading to difficulties in self-care, communication, and work-related tasks. As such, correct hand function assessment in PD is among the crucial aspects in evaluating motor impairment, in guiding treatment and tracking disease progression. Here, we report objective results obtained in assessing hand (dys)functionalities using an on-the-shelves fingerless sensory glove, named MANUS Quantum Metaglove, capable of sensing the variations of an electromagnetic field (EMF) sourced on the dorsal part of the hand and revealed by EMF coils at the fingers tips. A total of 65 people (35 PD patients and 30 healthy subjects for reference) were asked to perform standard motor tasks, and both most affected and least affected hands were assessed for opening-closing, grasping and pronation-supination movements. Differing from the generally adopted spatiotemporal analysis, taking a cue from non-linear theory adopted in electronics, we focused on spectral characteristics of the measured signals, specifically examining harmonic content and related harmonic distortions. As a result, we report how the adopted sensory glove, ensemble with spectral analysis, can be able to consistently assess hand motor (in)abilities in PD subjects and healthy subjects. In fact according to our results, PD patients significatively performed with hand motion signals affected by harmonic distortions, which revealed that the greater the complexity of the motor task, the greater the spread of the signal across harmonic frequencies, whilst healthy subjects perform with signals mostly around the fundamental frequency, as a marker of movement smoothness.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 507-514"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.bbe.2025.07.001
Patrizia Vizza , Giuseppe Tradigo , Pietro Hiram Guzzi , Pierangelo Veltri
Background and objectives: The identification, study and classification of anomalies in vocal signals are used to support physicians in the diagnosis and monitoring of vocal robe pathologies. Dysphonia is the most common disorder causing difficulties in voice production. Dysphonia refers to any impairment in voice quality, and significantly impacts on the quality of life. Early detection is imperative to prevent severe pathologies or to early detect chronic ones. Voice signal processing techniques, such as Fast Fourier Transform (FFT) and Praat, are noninvasive tools used to study phonatory apparatus diseases. Nevertheless there is room for improving efficacy in vocal signal patterns identification that could be related to vocal robe related pathologies.
Methods: The focus is on the possibility of using Goertzel Algorithm (GA) characteristics to improve state of the art for pattern identification in vocal signals. A tool for early identification of dysphonia based on GA is presented. An optimized version of GA, able to detect voice frequency anomalies has been implemented.
Results: The proposed tool has been tested with vocal signal datasets containing both normophonic and pathological subjects. The results are reported in terms of different implementation strategies and techniques. Experimental tests were performed comparing GA based and FFT based signal analysis tools in terms of: (i) efficiency and (ii) capacity of features identification. Performance parameters report: (i) an efficiency in terms of processing time improved by 37 % (i.e. 16.78 ms for FFT vs 12.26 ms for GA) and memory requirements reduced by 74 %; (ii) GA enabled the identification of healthy and pathological conditions better than FFT with a significance level below 0.05.
Conclusions: Results of using GA-based method on vocal signal processing, compared with existing methods, demonstrate the reliability of the proposed method in early identification of dysphonia and in clinical monitoring of patients post treatment.
{"title":"Dysphonia discovering using a Goertzel algorithm implementation for vocal signals analysis","authors":"Patrizia Vizza , Giuseppe Tradigo , Pietro Hiram Guzzi , Pierangelo Veltri","doi":"10.1016/j.bbe.2025.07.001","DOIUrl":"10.1016/j.bbe.2025.07.001","url":null,"abstract":"<div><div><em>Background and objectives:</em> The identification, study and classification of anomalies in vocal signals are used to support physicians in the diagnosis and monitoring of vocal robe pathologies. Dysphonia is the most common disorder causing difficulties in voice production. Dysphonia refers to any impairment in voice quality, and significantly impacts on the quality of life. Early detection is imperative to prevent severe pathologies or to early detect chronic ones. Voice signal processing techniques, such as Fast Fourier Transform (FFT) and Praat, are noninvasive tools used to study phonatory apparatus diseases. Nevertheless there is room for improving efficacy in vocal signal patterns identification that could be related to vocal robe related pathologies.</div><div><em>Methods:</em> The focus is on the possibility of using Goertzel Algorithm (GA) characteristics to improve state of the art for pattern identification in vocal signals. A tool for early identification of dysphonia based on GA is presented. An optimized version of GA, able to detect voice frequency anomalies has been implemented.</div><div><em>Results:</em> The proposed tool has been tested with vocal signal datasets containing both normophonic and pathological subjects. The results are reported in terms of different implementation strategies and techniques. Experimental tests were performed comparing GA based and FFT based signal analysis tools in terms of: (i) efficiency and (ii) capacity of features identification. Performance parameters report: (i) an efficiency in terms of processing time improved by 37 % (i.e. 16.78 ms for FFT vs 12.26 ms for GA) and memory requirements reduced by 74 %; (ii) GA enabled the identification of healthy and pathological conditions better than FFT with a significance level below 0.05.</div><div><em>Conclusions:</em> Results of using GA-based method on vocal signal processing, compared with existing methods, demonstrate the reliability of the proposed method in early identification of dysphonia and in clinical monitoring of patients post treatment.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 469-475"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.bbe.2025.08.002
Fabio Arthur Soares Araújo , Robson Luis Oliveira de Amorim , Marly Guimarães Fernandes Costa , Henrique Oliveira Martins , Cicero Ferreira Fernandes Costa Filho
Traumatic Brain Injury (TBI) remains a leading cause of morbidity and mortality worldwide, with significant disparities in outcomes influenced by regional healthcare access and infrastructure. This study evaluates the performance and generalizability of machine learning models for predicting 14-day mortality in TBI patients using datasets from two distinct Brazilian regions: São Paulo, an urban center, and Manaus, an isolated urban center with unique logistical challenges. To our knowledge, this research represents the first cross-validation of predictive models across two datasets within the same country, underscoring the critical need for localized approaches in TBI research. Our findings indicate that while convolutional neural network (CNN)-based models achieved high performance, with an area under the curve (AUC) of 0.90 in São Paulo and 0.93 in Manaus, the best model from São Paulo exhibited a strikingly low AUC when applied to the Manaus dataset. The incorporation of context-specific features, such as pandemic-related variables and time from trauma to admission, significantly enhanced model accuracy, with the Manaus model reaching an impressive AUC of 0.98. Notably, the study highlights key regional differences in predictors of mortality, with hypoxia and hypotension being more critical in Manaus, emphasizing the importance of tailoring predictive models to local contexts. These regionally important variables identified in the ML prediction model may inform quality-improvement priorities and further research in these settings. Our results indicate that CNN-based models have the potential to enhance mortality predictions for patients with traumatic brain injury (TBI).
{"title":"Evaluating the generalization of machine learning models for predicting 14-day mortality in traumatic brain injury patients","authors":"Fabio Arthur Soares Araújo , Robson Luis Oliveira de Amorim , Marly Guimarães Fernandes Costa , Henrique Oliveira Martins , Cicero Ferreira Fernandes Costa Filho","doi":"10.1016/j.bbe.2025.08.002","DOIUrl":"10.1016/j.bbe.2025.08.002","url":null,"abstract":"<div><div>Traumatic Brain Injury (TBI) remains a leading cause of morbidity and mortality worldwide, with significant disparities in outcomes influenced by regional healthcare access and infrastructure. This study evaluates the performance and generalizability of machine learning models for predicting 14-day mortality in TBI patients using datasets from two distinct Brazilian regions: São Paulo, an urban center, and Manaus, an isolated urban center with unique logistical challenges. To our knowledge, this research represents the first cross-validation of predictive models across two datasets within the same country, underscoring the critical need for localized approaches in TBI research. Our findings indicate that while convolutional neural network (CNN)-based models achieved high performance, with an area under the curve (AUC) of 0.90 in São Paulo and 0.93 in Manaus, the best model from São Paulo exhibited a strikingly low AUC when applied to the Manaus dataset. The incorporation of context-specific features, such as pandemic-related variables and time from trauma to admission, significantly enhanced model accuracy, with the Manaus model reaching an impressive AUC of 0.98. Notably, the study highlights key regional differences in predictors of mortality, with hypoxia and hypotension being more critical in Manaus, emphasizing the importance of tailoring predictive models to local contexts. These regionally important variables identified in the ML prediction model may inform quality-improvement priorities and further research in these settings. Our results indicate that CNN-based models have the potential to enhance mortality predictions for patients with traumatic brain injury (TBI).</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 560-571"},"PeriodicalIF":6.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Decoding pre-movement intention is crucial in developing a brain-computer interface (BCI) for neuro-rehabilitation robotic systems. However, the weak amplitude and non-smooth characteristics of EEG signals lead to the inability of existing methods to achieve the accuracy for proper applications. This study proposed a novel pre-movement intention decoding network framework to improve accuracy by extracting and optimizing the deep spatio-temporal features of EEG signals.
Methods
A deep spatio-temporal neural network structure was constructed based on the brain intention generation mechanism and its movement expression. The collected multi-channel EEG data were reorganized into brain topographic distributions, after the initial extraction of the features and optimization using the coordinate attention mechanism, a 3-layer dense block with two bi-directional gated recirculation units was designed to effectively extract the deep spatial and temporal features, further decoding the pre-movement intention efficiently.
Results
The experimental results showed an average accuracy of 95.51 ± 1.79 % for healthy subjects and 90.48 ± 2.90 % for stroke survivors in decoding pre-movement intention. All evaluation indexes are excellent. Pseudo-online testing showed the average TPR was 95.45 ± 3.80 % and 90.71 ± 7.77 % for healthy subjects and stroke survivors, respectively, and the latency was −1965 ± 48 ms and −1974 ± 36 ms. The results of the ablation and comparative analysis showed that the proposed framework is justified and its decoding capability outperforms other state-of-the-art algorithms.
Conclusion
The method proposed in this study has high decoding accuracy and good online performance in pre-movement intention decoding based on EEG signals, which lays the foundation for further neuro-rehabilitation robotic systems.
{"title":"Deep spatio-temporal features optimised fusion with coordinate attention mechanism for EEG lower limb pre-movement intention decoding","authors":"Runlin Dong , Xiaodong Zhang , Zhengzheng Zhou , Wenyu Zha , Aibin Zhu","doi":"10.1016/j.bbe.2025.06.004","DOIUrl":"10.1016/j.bbe.2025.06.004","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Decoding pre-movement intention is crucial in developing a brain-computer interface (BCI) for neuro-rehabilitation robotic systems. However, the weak amplitude and non-smooth characteristics of EEG signals lead to the inability of existing methods to achieve the accuracy for proper applications. This study proposed a novel pre-movement intention decoding network framework to improve accuracy by extracting and optimizing the deep spatio-temporal features of EEG signals.</div></div><div><h3>Methods</h3><div>A deep spatio-temporal neural network structure was constructed based on the brain intention generation mechanism and its movement expression. The collected multi-channel EEG data were reorganized into brain topographic distributions, after the initial extraction of the features and optimization using the coordinate attention mechanism, a 3-layer dense block with two bi-directional gated recirculation units was designed to effectively extract the deep spatial and temporal features, further decoding the pre-movement intention efficiently.</div></div><div><h3>Results</h3><div>The experimental results showed an average accuracy of 95.51 ± 1.79 % for healthy subjects and 90.48 ± 2.90 % for stroke survivors in decoding pre-movement intention. All evaluation indexes are excellent. Pseudo-online testing showed the average TPR was 95.45 ± 3.80 % and 90.71 ± 7.77 % for healthy subjects and stroke survivors, respectively, and the latency was −1965 ± 48 ms and −1974 ± 36 ms. The results of the ablation and comparative analysis showed that the proposed framework is justified and its decoding capability outperforms other state-of-the-art algorithms.</div></div><div><h3>Conclusion</h3><div>The method proposed in this study has high decoding accuracy and good online performance in pre-movement intention decoding based on EEG signals, which lays the foundation for further neuro-rehabilitation robotic systems.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 515-527"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.bbe.2025.08.005
Sayedmohsen Mortazavi Najafabadi , Dariusz Grzelczyk , Mohammed N. Ashtiani
Age, neurodegenerative diseases, diabetes, and sports injuries can all impair proprioception, i.e. a crucial sensory feedback system for balance control and gait. The purpose of this study was to assess the validity and reliability of a recently constructed device for measuring proprioceptive function. Forty-seven participants, comprising 26 younger healthy adults (20–40 years) and 21 older adults (> 65 years), were evaluated. The ankle’s sense of motion (SoM) sensitivity and sense of position (SoP, active/passive) acuity were measured by the device. The Intraclass Correlation Coefficient (ICC) and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) were used as indicators of reliability and validity. The results showed excellent reliability for SoM sensitivity in dorsiflexion (ICC = 0.985 for younger, 0.98 for older) and plantarflexion (ICC = 0.972 for younger, 0.982 for older). High reliability was also observed in passive SoP acuity (ICC = 0.825 – 0.989). However, the reliability of the active SoP acuity method was poor to moderate. Strong discriminative validity was demonstrated by the AUC-ROC values, with SoM sensitivity distinguishing between younger and older participants with an accuracy of over 91 %. Bland-Altman analysis revealed tighter agreement for SoM sensitivity (18 to 40 % of the device precision) than passive SoP acuity (70 to 90 % of the device precision), as well as minimal systematic bias (−0.03 to −0.01 degrees) to show interday test–retest reliability. According to these results, the device is valid for evaluating proprioceptive function, particularly SoM sensitivity, and it may be useful in clinical and research settings.
{"title":"A novel device for proprioceptive acuity measurement: Validity and reliability analysis in young and older adults","authors":"Sayedmohsen Mortazavi Najafabadi , Dariusz Grzelczyk , Mohammed N. Ashtiani","doi":"10.1016/j.bbe.2025.08.005","DOIUrl":"10.1016/j.bbe.2025.08.005","url":null,"abstract":"<div><div>Age, neurodegenerative diseases, diabetes, and sports injuries can all impair proprioception, i.e. a crucial sensory feedback system for balance control and gait. The purpose of this study was to assess the validity and reliability of a recently constructed device for measuring proprioceptive function. Forty-seven participants, comprising 26 younger healthy adults (20–40 years) and 21 older adults (> 65 years), were evaluated. The ankle’s sense of motion (SoM) sensitivity and sense of position (SoP, active/passive) acuity were measured by the device. The Intraclass Correlation Coefficient (ICC) and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) were used as indicators of reliability and validity. The results showed excellent reliability for SoM sensitivity in dorsiflexion (ICC = 0.985 for younger, 0.98 for older) and plantarflexion (ICC = 0.972 for younger, 0.982 for older). High reliability was also observed in passive SoP acuity (ICC = 0.825 – 0.989). However, the reliability of the active SoP acuity method was poor to moderate. Strong discriminative validity was demonstrated by the AUC-ROC values, with SoM sensitivity distinguishing between younger and older participants with an accuracy of over 91 %. Bland-Altman analysis revealed tighter agreement for SoM sensitivity (18 to 40 % of the device precision) than passive SoP acuity (70 to 90 % of the device precision), as well as minimal systematic bias (−0.03 to −0.01 degrees) to show interday test–retest reliability. According to these results, the device is valid for evaluating proprioceptive function, particularly SoM sensitivity, and it may be useful in clinical and research settings.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 580-589"},"PeriodicalIF":6.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The continuous negative external pressure (cNEP) applied on the submental surface is a method of non-surgical treatment for obstructive sleep apnea (OSA), which can effectively widen the airway in some OSA patients. However, it cannot effectively improve airway collapse in obese patients and its mechanism remains unclear. In this study, we aim to analyze the reasons for the ineffectiveness of cNEP treatment in OSA patients with obesity. Based on magnetic resonance imaging (MRI), three-dimensional models of the head and neck were constructed for a healthy subject, an OSA patient with enlarged tongue, and an OSA patient with the tongue adjusted to normal size. By performing the one step staggered fluid–structure interaction numerical simulations, we analyzed the collapse of the airway in these three models under the influence of cNEP. Restoring the tongue to normal size in the OSA patient significantly improves the airway critical closing pressure under cNEP treatment compared to the patient with enlarged tongue. The enlargement of the tongue in the OSA patient hindered the widening of the velopharyngeal airway under the action of cNEP. The numerical results reveal that cNEP treatment can effectively widen the laryngopharyngeal airway, thus providing a potential therapeutic option for OSA patients with laryngopharyngeal obstruction. Tongue enlargement in OSA patients is a critical factor influencing the efficacy of cNEP treatment. This study reveals the reasons for cNEP treatment failure in obese patients and the potential value of cNEP targeted therapy.
{"title":"The impact of tongue size on submental negative pressure treatment of airway obstruction revealed by fluid-structure interaction simulations","authors":"Yuhang Tian , Huahui Xiong , Hui Tong , Changjin Ji , Xiaoqing Huang , Yaqi Huang","doi":"10.1016/j.bbe.2025.08.004","DOIUrl":"10.1016/j.bbe.2025.08.004","url":null,"abstract":"<div><div>The continuous negative external pressure (cNEP) applied on the submental surface is a method of non-surgical treatment for obstructive sleep apnea (OSA), which can effectively widen the airway in some OSA patients. However, it cannot effectively improve airway collapse in obese patients and its mechanism remains unclear. In this study, we aim to analyze the reasons for the ineffectiveness of cNEP treatment in OSA patients with obesity. Based on magnetic resonance imaging (MRI), three-dimensional models of the head and neck were constructed for a healthy subject, an OSA patient with enlarged tongue, and an OSA patient with the tongue adjusted to normal size. By performing the one step staggered fluid–structure interaction numerical simulations, we analyzed the collapse of the airway in these three models under the influence of cNEP. Restoring the tongue to normal size in the OSA patient significantly improves the airway critical closing pressure under cNEP treatment compared to the patient with enlarged tongue. The enlargement of the tongue in the OSA patient hindered the widening of the velopharyngeal airway under the action of cNEP. The numerical results reveal that cNEP treatment can effectively widen the laryngopharyngeal airway, thus providing a potential therapeutic option for OSA patients with laryngopharyngeal obstruction. Tongue enlargement in OSA patients is a critical factor influencing the efficacy of cNEP treatment. This study reveals the reasons for cNEP treatment failure in obese patients and the potential value of cNEP targeted therapy.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 590-599"},"PeriodicalIF":6.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-27DOI: 10.1016/j.bbe.2025.06.001
Marcin Sińczuk , Jacek Rogala , Piotr Bogorodzki
This study explores the importance of scanner-specific calibration measurements for Magnetic Resonance Spectroscopy Thermometry (MRST) in human brain temperature estimations. Data acquisition was conducted on a 3-T GE scanner. Calibration constants for the water-chemical shift were obtained using a temperature-controlled phantom containing an aqueous solution of N-acetyl aspartate (NAA), Creatine (Cr), and Choline (Cho), and data from three different research groups using the same metabolites. Temperatures were estimated utilizing correlation of water chemical shift with NAA, Cr and Cho. For data acquisition, commercially available single-voxel point-resolved spectroscopy (PRESS) sequences were used for calibrations and in vivo temperature estimations. Each sequence included spectras without (WU) and with (WS) water suppression. In vivo study consisted of two PRESS sequences, one before and one after extensive 30-minute fMRI task acquisition. Significant differences were found between absolute brain temperatures measured using scanner-specific calibrations and those from other researchers, varying from −0.68 °C to + 0.37 °C for NAA, −0.92 °C to 0.37 °C for Cr, and −0.78 °C to 0.7 °C for Cho. Each method reported a similar temperature decrease of −0.26 ∓ 0.03 °C between before and after fMRI measurements. These findings suggest that while absolute temperatures from non-scanner specific calibrations may be inaccurate, comparative estimates are valid.
{"title":"MRS thermometry – Importance of scanner-specific calibrations for accurate brain temperature estimations","authors":"Marcin Sińczuk , Jacek Rogala , Piotr Bogorodzki","doi":"10.1016/j.bbe.2025.06.001","DOIUrl":"10.1016/j.bbe.2025.06.001","url":null,"abstract":"<div><div>This study explores the importance of scanner-specific calibration measurements for Magnetic Resonance Spectroscopy Thermometry (MRST) in human brain temperature estimations. Data acquisition was conducted on a 3-T GE scanner. Calibration constants for the water-chemical shift were obtained using a temperature-controlled phantom containing an aqueous solution of N-acetyl aspartate (NAA), Creatine (Cr), and Choline (Cho), and data from three different research groups using the same metabolites. Temperatures were estimated utilizing correlation of water chemical shift with NAA, Cr and Cho. For data acquisition, commercially available single-voxel point-resolved spectroscopy (PRESS) sequences were used for calibrations and in vivo temperature estimations. Each sequence included spectras without (WU) and with (WS) water suppression. In vivo study consisted of two PRESS sequences, one before and one after extensive 30-minute fMRI task acquisition. Significant differences were found between absolute brain temperatures measured using scanner-specific calibrations and those from other researchers, varying from −0.68 °C to + 0.37 °C for NAA, −0.92 °C to 0.37 °C for Cr, and −0.78 °C to 0.7 °C for Cho. Each method reported a similar temperature decrease of −0.26 ∓ 0.03 °C between before and after fMRI measurements. These findings suggest that while absolute temperatures from non-scanner specific calibrations may be inaccurate, comparative estimates are valid.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 451-456"},"PeriodicalIF":5.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-27DOI: 10.1016/j.bbe.2025.06.002
Natalia Kowalczyk, Jacek Rumiński, Magdalena Mazur-Milecka
The COVID-19 pandemic has underscored the importance of wearing facial masks and monitoring respiratory health to prevent the spread of the virus. In this study, we developed a model for segmenting facial masks in thermal images. We applied the model to segment face masks in different conditions, including a person walking toward the observing camera. The segmented regions were further processed using different erosion masks to analyze the influence of the selected sources on the quality of the estimated respiratory signals. The Signal-to-Noise Ratio (SNR) was used as a quality measure. Additionally, the extracted respiratory signals were compared with two reference signals: binary signals generated by participants who signaled the inhalation phase and pressure signals measured with a respiratory belt. Our findings show a high level of concordance between the respiratory signals derived from the segmented mask region and those from the respiratory belt, validating the effectiveness of thermal imaging for capturing respiratory patterns. Notably, the signal-to-noise ratio (SNR) was higher for the segmented mask than the detection methods used in previous works. Specifically, for the mask segmentation task, the mean SNR improved by 4.3 compared to facial mask detection. The segmentation model achieved a mean Average Precision (mAP) of 0.992 for segmentation tasks and 0.857 mAP at the 50–95 % threshold using the Yolov8 “nano” architecture. This study underscores the potential of thermal imaging for non-invasive respiratory monitoring and highlights the explainability and accuracy of selecting the facial mask region for signal extraction.
{"title":"Improving the quality of respiratory signals extracted from the segmented mask area","authors":"Natalia Kowalczyk, Jacek Rumiński, Magdalena Mazur-Milecka","doi":"10.1016/j.bbe.2025.06.002","DOIUrl":"10.1016/j.bbe.2025.06.002","url":null,"abstract":"<div><div>The COVID-19 pandemic has underscored the importance of wearing facial masks and monitoring respiratory health to prevent the spread of the virus. In this study, we developed a model for segmenting facial masks in thermal images. We applied the model to segment face masks in different conditions, including a person walking toward the observing camera. The segmented regions were further processed using different erosion masks to analyze the influence of the selected sources on the quality of the estimated respiratory signals. The Signal-to-Noise Ratio (SNR) was used as a quality measure. Additionally, the extracted respiratory signals were compared with two reference signals: binary signals generated by participants who signaled the inhalation phase and pressure signals measured with a respiratory belt. Our findings show a high level of concordance between the respiratory signals derived from the segmented mask region and those from the respiratory belt, validating the effectiveness of thermal imaging for capturing respiratory patterns. Notably, the signal-to-noise ratio (SNR) was higher for the segmented mask than the detection methods used in previous works. Specifically, for the mask segmentation task, the mean SNR improved by 4.3 compared to facial mask detection. The segmentation model achieved a mean Average Precision (mAP) of 0.992 for segmentation tasks and 0.857 mAP at the 50–95 % threshold using the Yolov8 “nano” architecture. This study underscores the potential of thermal imaging for non-invasive respiratory monitoring and highlights the explainability and accuracy of selecting the facial mask region for signal extraction.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 457-468"},"PeriodicalIF":5.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-14DOI: 10.1016/j.bbe.2025.05.011
Víctor Gutiérrez-de Pablo , María Herrero-Tudela , Marina Sandonís-Fernández , Jesús Poza , Aarón Maturana-Candelas , Víctor Rodríguez-González , Miguel Ángel Tola-Arribas , Mónica Cano , Hideyuki Hoshi , Yoshihito Shigihara , Roberto Hornero , Carlos Gómez
Dementia and mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) are neurological pathologies associated with disruptions in brain electromagnetic activity, typically studied using magnetoencephalography (MEG) and electroencephalography (EEG). To quantify diverse brain properties, different families of parameters can be computed from MEG and EEG (i.e., spectral, non-linear, morphological, functional connectivity, or network structure and organisation). However, studying these characteristics separately overlooks the complex nature of brain activity. Integrative frameworks can be useful to unveil the intricate neurophysiological fingerprint, as well as to characterise pathological conditions comprehensively. To that purpose, data fusion methodologies are crucial, despite their interpretational challenges. In this study, Machine Learning (ML) models were trained to discriminate between groups of severity, whereas the SHapley Additive eXplanations (SHAP) algorithm was afterwards utilised to assess the relevance of the input characteristics into the output classification. Three databases were analysed: MEG (55 healthy controls, HC, 42 MCI patients, and 86 AD patients), EEG1 (51 HC, 52 MCI, and 100 AD), and EEG2 (45 HC, 69 MCI, and 82 AD). The best results for the three-class classification problem were obtained by Gradient Boosting for the MEG database: 3-class Cohen’s kappa coefficient of 0.5452 and accuracy of 72.63 %. Afterwards, using SHAP on Gradient Boosting, it has been shown that spectral features were identified as highly relevant across all databases. Furthermore, morphology measures presented high relevance for the MEG database, whereas EEG1 and EEG2 databases showed functional connectivity and multiplex organisation measures, respectively, as relevant subgroups of parameters. Finally, commonly relevant features across databases were selected using SHAP to generate the neurophysiological fingerprints of AD and MCI. This study highlights the relevance of different MEG and EEG parameters in characterising neurological pathologies. The proposed framework, based on MEG and EEG, can be used to generate interpretable, robust, and accurate neurophysiological fingerprints of AD and MCI.
{"title":"Integrative and interpretable framework to unveil the neurophysiological fingerprint of Alzheimer’s disease and mild cognitive impairment: A machine learning-SHAP approach","authors":"Víctor Gutiérrez-de Pablo , María Herrero-Tudela , Marina Sandonís-Fernández , Jesús Poza , Aarón Maturana-Candelas , Víctor Rodríguez-González , Miguel Ángel Tola-Arribas , Mónica Cano , Hideyuki Hoshi , Yoshihito Shigihara , Roberto Hornero , Carlos Gómez","doi":"10.1016/j.bbe.2025.05.011","DOIUrl":"10.1016/j.bbe.2025.05.011","url":null,"abstract":"<div><div>Dementia and mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) are neurological pathologies associated with disruptions in brain electromagnetic activity, typically studied using magnetoencephalography (MEG) and electroencephalography (EEG). To quantify diverse brain properties, different families of parameters can be computed from MEG and EEG (i.e., spectral, non-linear, morphological, functional connectivity, or network structure and organisation). However, studying these characteristics separately overlooks the complex nature of brain activity. Integrative frameworks can be useful to unveil the intricate neurophysiological fingerprint, as well as to characterise pathological conditions comprehensively. To that purpose, data fusion methodologies are crucial, despite their interpretational challenges. In this study, Machine Learning (ML) models were trained to discriminate between groups of severity, whereas the SHapley Additive eXplanations (SHAP) algorithm was afterwards utilised to assess the relevance of the input characteristics into the output classification. Three databases were analysed: MEG (55 healthy controls, HC, 42 MCI patients, and 86 AD patients), EEG1 (51 HC, 52 MCI, and 100 AD), and EEG2 (45 HC, 69 MCI, and 82 AD). The best results for the three-class classification problem were obtained by Gradient Boosting for the MEG database: 3-class Cohen’s kappa coefficient of 0.5452 and accuracy of 72.63 %. Afterwards, using SHAP on Gradient Boosting, it has been shown that spectral features were identified as highly relevant across all databases. Furthermore, morphology measures presented high relevance for the MEG database, whereas EEG1 and EEG2 databases showed functional connectivity and multiplex organisation measures, respectively, as relevant subgroups of parameters. Finally, commonly relevant features across databases were selected using SHAP to generate the neurophysiological fingerprints of AD and MCI. This study highlights the relevance of different MEG and EEG parameters in characterising neurological pathologies. The proposed framework, based on MEG and EEG, can be used to generate interpretable, robust, and accurate neurophysiological fingerprints of AD and MCI.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 438-450"},"PeriodicalIF":5.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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