Pub Date : 2023-12-18DOI: 10.1109/JTEHM.2023.3344035
Hojjat Salehinejad;Anne M. Meehan;Pedro J. Caraballo;Bijan J. Borah
Objective: Deterioration index (DI) is a computer-generated score at a specific frequency that represents the overall condition of hospitalized patients using a variety of clinical, laboratory and physiologic data. In this paper, a contrastive transfer learning method is proposed and validated for early prediction of adverse events in hospitalized patients using DI scores. Methods and procedures: An unsupervised contrastive learning (CL) model with a classifier is proposed to predict adverse outcome using a single temporal variable (DI scores). The model is pretrained on an unsupervised fashion with large-scale time series data and fine-tuned with retrospective DI score data. Results: The performance of this model is compared with supervised deep learning models for time series classification. Results show that unsupervised contrastive transfer learning with a classifier outperforms supervised deep learning solutions. Pretraining of the proposed CL model with large-scale time series data and fine-tuning that with DI scores can enhance prediction accuracy. Conclusion: A relationship exists between longitudinal DI scores of a patient and the corresponding outcome. DI scores and contrastive transfer learning can be used to predict and prevent adverse outcomes in hospitalized patients. Clinical impact: This paper successfully developed an unsupervised contrastive transfer learning algorithm for prediction of adverse events in hospitalized patients. The proposed model can be deployed in hospitals as an early warning system for preemptive intervention in hospitalized patients, which can mitigate the likelihood of adverse outcomes.
目的：恶化指数（DI恶化指数（DI）是一种计算机生成的特定频率的分数，它利用各种临床、实验室和生理数据来代表住院患者的整体状况。本文提出并验证了一种对比迁移学习方法，用于利用 DI 评分早期预测住院患者的不良事件。方法和程序：本文提出了一种带有分类器的无监督对比学习（CL）模型，利用单一时间变量（DI 评分）预测不良后果。该模型利用大规模时间序列数据进行无监督预训练，并利用回顾性 DI 评分数据进行微调。结果：该模型的性能与用于时间序列分类的有监督深度学习模型进行了比较。结果表明，带有分类器的无监督对比迁移学习优于有监督深度学习解决方案。利用大规模时间序列数据对所提出的 CL 模型进行预训练，并利用 DI 分数对其进行微调，可以提高预测准确性。结论患者的纵向 DI 分数与相应的结果之间存在关系。DI 评分和对比迁移学习可用于预测和预防住院患者的不良预后。临床影响：本文成功开发了一种用于预测住院患者不良事件的无监督对比迁移学习算法。所提出的模型可作为预警系统部署在医院中，对住院病人进行先期干预，从而降低不良后果发生的可能性。
{"title":"Contrastive Transfer Learning for Prediction of Adverse Events in Hospitalized Patients","authors":"Hojjat Salehinejad;Anne M. Meehan;Pedro J. Caraballo;Bijan J. Borah","doi":"10.1109/JTEHM.2023.3344035","DOIUrl":"https://doi.org/10.1109/JTEHM.2023.3344035","url":null,"abstract":"Objective: Deterioration index (DI) is a computer-generated score at a specific frequency that represents the overall condition of hospitalized patients using a variety of clinical, laboratory and physiologic data. In this paper, a contrastive transfer learning method is proposed and validated for early prediction of adverse events in hospitalized patients using DI scores. Methods and procedures: An unsupervised contrastive learning (CL) model with a classifier is proposed to predict adverse outcome using a single temporal variable (DI scores). The model is pretrained on an unsupervised fashion with large-scale time series data and fine-tuned with retrospective DI score data. Results: The performance of this model is compared with supervised deep learning models for time series classification. Results show that unsupervised contrastive transfer learning with a classifier outperforms supervised deep learning solutions. Pretraining of the proposed CL model with large-scale time series data and fine-tuning that with DI scores can enhance prediction accuracy. Conclusion: A relationship exists between longitudinal DI scores of a patient and the corresponding outcome. DI scores and contrastive transfer learning can be used to predict and prevent adverse outcomes in hospitalized patients. Clinical impact: This paper successfully developed an unsupervised contrastive transfer learning algorithm for prediction of adverse events in hospitalized patients. The proposed model can be deployed in hospitals as an early warning system for preemptive intervention in hospitalized patients, which can mitigate the likelihood of adverse outcomes.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"215-224"},"PeriodicalIF":3.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10363391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034302","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 : 2023-12-07DOI: 10.1109/JTEHM.2023.3340345
Dosti Aziz;Sztahó Dávid
Objective: Despite speech being the primary communication medium, it carries valuable information about a speaker’s health, emotions, and identity. Various conditions can affect the vocal organs, leading to speech difficulties. Extensive research has been conducted by voice clinicians and academia in speech analysis. Previous approaches primarily focused on one particular task, such as differentiating between normal and dysphonic speech, classifying different voice disorders, or estimating the severity of voice disorders. Methods and procedures: This study proposes an approach that combines transfer learning and multitask learning (MTL) to simultaneously perform dysphonia classification and severity estimation. Both tasks use a shared representation; network is learned from these shared features. We employed five computer vision models and changed their architecture to support multitask learning. Additionally, we conducted binary ‘healthy vs. dysphonia’ and multiclass ‘healthy vs. organic and functional dysphonia’ classification using multitask learning, with the speaker’s sex as an auxiliary task. Results: The proposed method achieved improved performance across all classification metrics compared to single-task learning (STL), which only performs classification or severity estimation. Specifically, the model achieved F1 scores of 93% and 90% in MTL and STL, respectively. Moreover, we observed considerable improvements in both classification tasks by evaluating beta values associated with the weight assigned to the sex-predicting auxiliary task. MTL achieved an accuracy of 77% compared to the STL score of 73.2%. However, the performance of severity estimation in MTL was comparable to STL. Conclusion: Our goal is to improve how voice pathologists and clinicians understand patients’ conditions, make it easier to track their progress, and enhance the monitoring of vocal quality and treatment procedures. Clinical and Translational Impact Statement: By integrating both classification and severity estimation of dysphonia using multitask learning, we aim to enable clinicians to gain a better understanding of the patient’s situation, effectively monitor their progress and voice quality.
目的：尽管语言是主要的交流媒介，但它也承载着有关说话者健康、情感和身份的宝贵信息。各种疾病都会影响发声器官，导致说话困难。嗓音临床医生和学术界对语音分析进行了广泛的研究。以往的方法主要集中于某一特定任务，如区分正常语音和发音障碍语音、对不同嗓音疾病进行分类或估计嗓音疾病的严重程度。方法和程序：本研究提出了一种结合迁移学习和多任务学习（MTL）的方法，可同时进行发音障碍分类和严重程度评估。这两项任务都使用共享表征；网络是从这些共享特征中学习的。我们采用了五种计算机视觉模型，并改变了它们的架构以支持多任务学习。此外，我们还使用多任务学习进行了二元 "健康 vs. 发声困难 "和多类 "健康 vs. 器质性和功能性发声困难 "分类，并将说话者的性别作为辅助任务。结果与只进行分类或严重程度估计的单任务学习（STL）相比，所提出的方法在所有分类指标上都取得了更好的性能。具体来说，该模型在 MTL 和 STL 中的 F1 分数分别达到了 93% 和 90%。此外，通过评估与分配给性别预测辅助任务的权重相关的贝塔值，我们还观察到这两项分类任务都有相当大的改进。MTL 的准确率为 77%，而 STL 为 73.2%。不过，MTL 在严重程度估计方面的表现与 STL 不相上下。结论我们的目标是改善嗓音病理学家和临床医生对患者病情的了解，使他们更容易跟踪病情进展，并加强对嗓音质量和治疗过程的监控。临床和转化影响声明：通过使用多任务学习将发音障碍的分类和严重程度评估结合起来，我们的目标是让临床医生更好地了解患者的情况，有效地监测他们的病情进展和嗓音质量。
{"title":"Multitask and Transfer Learning Approach for Joint Classification and Severity Estimation of Dysphonia","authors":"Dosti Aziz;Sztahó Dávid","doi":"10.1109/JTEHM.2023.3340345","DOIUrl":"https://doi.org/10.1109/JTEHM.2023.3340345","url":null,"abstract":"Objective: Despite speech being the primary communication medium, it carries valuable information about a speaker’s health, emotions, and identity. Various conditions can affect the vocal organs, leading to speech difficulties. Extensive research has been conducted by voice clinicians and academia in speech analysis. Previous approaches primarily focused on one particular task, such as differentiating between normal and dysphonic speech, classifying different voice disorders, or estimating the severity of voice disorders. Methods and procedures: This study proposes an approach that combines transfer learning and multitask learning (MTL) to simultaneously perform dysphonia classification and severity estimation. Both tasks use a shared representation; network is learned from these shared features. We employed five computer vision models and changed their architecture to support multitask learning. Additionally, we conducted binary ‘healthy vs. dysphonia’ and multiclass ‘healthy vs. organic and functional dysphonia’ classification using multitask learning, with the speaker’s sex as an auxiliary task. Results: The proposed method achieved improved performance across all classification metrics compared to single-task learning (STL), which only performs classification or severity estimation. Specifically, the model achieved F1 scores of 93% and 90% in MTL and STL, respectively. Moreover, we observed considerable improvements in both classification tasks by evaluating beta values associated with the weight assigned to the sex-predicting auxiliary task. MTL achieved an accuracy of 77% compared to the STL score of 73.2%. However, the performance of severity estimation in MTL was comparable to STL. Conclusion: Our goal is to improve how voice pathologists and clinicians understand patients’ conditions, make it easier to track their progress, and enhance the monitoring of vocal quality and treatment procedures. Clinical and Translational Impact Statement: By integrating both classification and severity estimation of dysphonia using multitask learning, we aim to enable clinicians to gain a better understanding of the patient’s situation, effectively monitor their progress and voice quality.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"233-244"},"PeriodicalIF":3.4,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034042","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}
Malignant glioma (MG) is the most common type of primary malignant brain tumors. Surgical resection of MG remains the cornerstone of therapy and the extent of resection correlates with patient survival. A limiting factor for resection, however, is the difficulty in differentiating the tumor from normal tissue during surgery. Fluorescence imaging is an emerging technique for real-time intraoperative visualization of MGs and their boundaries. However, most clinical grade neurosurgical operative microscopes with fluorescence imaging ability are hampered by low adoption rates due to high cost, limited portability, limited operation flexibility, and lack of skilled professionals with technical knowledge. To overcome the limitations, we innovatively integrated miniaturized light sources, flippable filters, and a recording camera to the surgical eye loupes to generate a wearable fluorescence eye loupe (FLoupe) device for intraoperative imaging of fluorescent MGs. Two FLoupe prototypes were constructed for imaging of Fluorescein and 5-aminolevulinic acid (5-ALA), respectively. The wearable FLoupe devices were tested on tumor-simulating phantoms and patients with MGs. Comparable results were observed against the standard neurosurgical operative microscope (PENTERO® 900) with fluorescence kits. The affordable and wearable FLoupe devices enable visualization of both color and fluorescence images with the same quality as the large and expensive stationary operative microscopes. The wearable FLoupe device allows for a greater range of movement, less obstruction, and faster/easier operation. Thus, it reduces surgery time and is more easily adapted to the surgical environment than unwieldy neurosurgical operative microscopes. Clinical and Translational Impact Statement—The affordable and wearable fluorescence imaging device developed in this study enables neurosurgeons to observe brain tumors with the same clarity and greater flexibility compared to bulky and costly operative microscopes.
{"title":"A Wearable Fluorescence Imaging Device for Intraoperative Identification of Human Brain Tumors","authors":"Mehrana Mohtasebi;Chong Huang;Mingjun Zhao;Siavash Mazdeyasna;Xuhui Liu;Samaneh Rabienia Haratbar;Faraneh Fathi;Jinghong Sun;Thomas Pittman;Guoqiang Yu","doi":"10.1109/JTEHM.2023.3338564","DOIUrl":"https://doi.org/10.1109/JTEHM.2023.3338564","url":null,"abstract":"Malignant glioma (MG) is the most common type of primary malignant brain tumors. Surgical resection of MG remains the cornerstone of therapy and the extent of resection correlates with patient survival. A limiting factor for resection, however, is the difficulty in differentiating the tumor from normal tissue during surgery. Fluorescence imaging is an emerging technique for real-time intraoperative visualization of MGs and their boundaries. However, most clinical grade neurosurgical operative microscopes with fluorescence imaging ability are hampered by low adoption rates due to high cost, limited portability, limited operation flexibility, and lack of skilled professionals with technical knowledge. To overcome the limitations, we innovatively integrated miniaturized light sources, flippable filters, and a recording camera to the surgical eye loupes to generate a wearable fluorescence eye loupe (FLoupe) device for intraoperative imaging of fluorescent MGs. Two FLoupe prototypes were constructed for imaging of Fluorescein and 5-aminolevulinic acid (5-ALA), respectively. The wearable FLoupe devices were tested on tumor-simulating phantoms and patients with MGs. Comparable results were observed against the standard neurosurgical operative microscope (PENTERO® 900) with fluorescence kits. The affordable and wearable FLoupe devices enable visualization of both color and fluorescence images with the same quality as the large and expensive stationary operative microscopes. The wearable FLoupe device allows for a greater range of movement, less obstruction, and faster/easier operation. Thus, it reduces surgery time and is more easily adapted to the surgical environment than unwieldy neurosurgical operative microscopes. Clinical and Translational Impact Statement—The affordable and wearable fluorescence imaging device developed in this study enables neurosurgeons to observe brain tumors with the same clarity and greater flexibility compared to bulky and costly operative microscopes.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"225-232"},"PeriodicalIF":3.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10339301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081243","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 : 2023-11-27DOI: 10.1109/JTEHM.2023.3336889
Kaya Kuru;Darren Ansell;Dave Hughes;Benjamin Jon Watkinson;Fabrizio Gaudenzi;Martin Jones;David Lunardi;Noreen Caswell;Adela Rabella Montiel;Peter Leather;Daniel Irving;Kina Bennett;Corrin McKenzie;Paula Sugden;Carl Davies;Christian Degoede
Our study was designed to develop a customisable, wearable, and comfortable medical device – the text so-called “MyPAD” that monitors the fullness of the bladder, triggering an alarm indicating the need to void, in order to prevent badwetting – i.e., treating Nocturnal Enuresis (NE) at the text pre-void stage using miniaturised mechatronics with Artificial Intelligence (AI). The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the text tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses. Clinical impact: MyPAD will treat the NE better and efficiently against other techniques currently used (e.g., post-void alarms) and will i) replace those techniques quickly considering sufferers’ condition while being treated by other approaches, and ii) enable children to gain control of incontinence over time and consistently have dry nights. Category: Early/Pre-Clinical Research
{"title":"Treatment of Nocturnal Enuresis Using Miniaturised Smart Mechatronics With Artificial Intelligence","authors":"Kaya Kuru;Darren Ansell;Dave Hughes;Benjamin Jon Watkinson;Fabrizio Gaudenzi;Martin Jones;David Lunardi;Noreen Caswell;Adela Rabella Montiel;Peter Leather;Daniel Irving;Kina Bennett;Corrin McKenzie;Paula Sugden;Carl Davies;Christian Degoede","doi":"10.1109/JTEHM.2023.3336889","DOIUrl":"https://doi.org/10.1109/JTEHM.2023.3336889","url":null,"abstract":"Our study was designed to develop a customisable, wearable, and comfortable medical device – the text so-called “MyPAD” that monitors the fullness of the bladder, triggering an alarm indicating the need to void, in order to prevent badwetting – i.e., treating Nocturnal Enuresis (NE) at the text pre-void stage using miniaturised mechatronics with Artificial Intelligence (AI). The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the text tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses. Clinical impact: MyPAD will treat the NE better and efficiently against other techniques currently used (e.g., post-void alarms) and will i) replace those techniques quickly considering sufferers’ condition while being treated by other approaches, and ii) enable children to gain control of incontinence over time and consistently have dry nights. Category: Early/Pre-Clinical Research","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"204-214"},"PeriodicalIF":3.4,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10328832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138485029","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 : 2023-11-21DOI: 10.1109/JTEHM.2023.3335608
Andrea Moglia;Luca Marsilio;Matteo Rossi;Maria Pinelli;Emanuele Lettieri;Luca Mainardi;Alfonso Manzotti;Pietro Cerveri
Objective: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset. Methods: Two preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability. Results: During the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks (“Patient selection” and “Scrolling through radiograph”) with respect to the second attempt, but without statistically significant difference (respectively $p$ = 0.14 and $p$ = 0.13, $p < 0.05$ ). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9%) strongly agreed that it can be used effectively for preoperative planning. Six (54.5%) agreed and two of them (18.2%) strongly agreed that it can be used effectively for intraoperative guidance. Discussion/Conclusion: In this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage.
{"title":"Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy","authors":"Andrea Moglia;Luca Marsilio;Matteo Rossi;Maria Pinelli;Emanuele Lettieri;Luca Mainardi;Alfonso Manzotti;Pietro Cerveri","doi":"10.1109/JTEHM.2023.3335608","DOIUrl":"https://doi.org/10.1109/JTEHM.2023.3335608","url":null,"abstract":"Objective: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset. Methods: Two preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability. Results: During the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks (“Patient selection” and “Scrolling through radiograph”) with respect to the second attempt, but without statistically significant difference (respectively \u0000<inline-formula> <tex-math>$p$ </tex-math></inline-formula>\u0000 = 0.14 and \u0000<inline-formula> <tex-math>$p$ </tex-math></inline-formula>\u0000 = 0.13, \u0000<inline-formula> <tex-math>$p < 0.05$ </tex-math></inline-formula>\u0000). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9%) strongly agreed that it can be used effectively for preoperative planning. Six (54.5%) agreed and two of them (18.2%) strongly agreed that it can be used effectively for intraoperative guidance. Discussion/Conclusion: In this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"279-290"},"PeriodicalIF":3.4,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10325506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676277","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}
This work aims to explore the utility of wearable inertial measurement units (IMUs) for quantifying movement in Romberg tests and investigate the extent of movement in adults with vestibular hypofunction (VH). A cross-sectional study was conducted at an academic tertiary medical center between March 2021 and April 2022. Adults diagnosed with unilateral vestibular hypofunction (UVH) or bilateral vestibular hypofunction (BVH) were enrolled in the VH group. Healthy controls (HCs) were recruited from community or outpatient clinics. The IMU-based instrumented Romberg and tandem Romberg tests on the floor were applied to both groups. The primary outcomes were kinematic body metrics (maximum acceleration [ACC], mean ACC, root mean square [RMS] of ACC, and mean sway velocity [MV]) along the medio-lateral (ML), cranio-caudal (CC), and antero-posterior (AP) axes. A total of 31 VH participants (mean age, 33.48 [SD 7.68] years; 19 [61%] female) and 31 HCs (mean age, 30.65 [SD 5.89] years; 18 [58%] female) were recruited. During the eyes-closed portion of the Romberg test, VH participants demonstrated significantly higher maximum ACC and increased RMS of ACC in head movement, as well as higher maximum ACC in pelvic movement along the ML axis. In the same test condition, individuals with BVH exhibited notably higher maximum ACC and RMS of ACC along the ML axis in head and pelvic movements compared with HCs. Additionally, BVH participants exhibited markedly increased maximum ACC along the ML axis in head movement during the eyes-open portion of the tandem Romberg test. Conversely, no significant differences were found between UVH participants and HCs in the assessed parameters. The instrumented Romberg and tandem Romberg tests characterized the kinematic differences in head, pelvis, and ankle movement between VH and healthy adults. The findings suggest that these kinematic body metrics can be useful for screening BVH and can provide goals for vestibular rehabilitation.
这项研究旨在探索可穿戴惯性测量单元（IMU）在朗伯格测试中量化运动的实用性，并调查患有前庭功能减退（VH）的成年人的运动程度。一项横断面研究于 2021 年 3 月至 2022 年 4 月在一家学术性三级医疗中心进行。被诊断为单侧前庭功能减退（UVH）或双侧前庭功能减退（BVH）的成人被纳入 VH 组。健康对照组（HC）从社区或门诊诊所招募。两组均采用基于 IMU 的仪器朗伯格测试和地面串联朗伯格测试。主要结果是沿内侧轴（ML）、颅尾轴（CC）和前后轴（AP）的身体运动学指标（最大加速度[ACC]、平均加速度[ACC]、加速度[ACC]均方根[RMS]和平均摇摆速度[MV]）。共招募了 31 名 VH 参与者（平均年龄 33.48 [SD 7.68] 岁；19 [61%] 名女性）和 31 名 HC 参与者（平均年龄 30.65 [SD 5.89] 岁；18 [58%] 名女性）。在朗伯格测试的闭眼部分，VH 参与者在头部运动中表现出明显更高的最大 ACC 值和更高的 ACC RMS 值，在骨盆沿 ML 轴运动中也表现出更高的最大 ACC 值。在相同的测试条件下，BVH 患者在头部和骨盆运动中沿 ML 轴的最大 ACC 和 ACC 均方根都明显高于 HC 患者。此外，在串联朗伯格测试的睁眼部分，BVH 参与者在头部运动中表现出沿 ML 轴的最大 ACC 明显增加。相反，UVH 参与者和 HC 在评估参数上没有发现明显差异。用仪器进行的朗伯格测试和串联朗伯格测试显示了超视力障碍成人和健康成人在头部、骨盆和踝关节运动方面的运动学差异。研究结果表明，这些身体运动学指标可用于筛查前庭功能障碍，并为前庭康复提供目标。
{"title":"Inertial Measurement Unit-Based Romberg Test for Assessing Adults With Vestibular Hypofunction","authors":"Kuan-Chung Ting;Yu-Chieh Lin;Chia-Tai Chan;Tzong-Yang Tu;Chun-Che Shih;Kai-Chun Liu;Yu Tsao","doi":"10.1109/JTEHM.2023.3334238","DOIUrl":"https://doi.org/10.1109/JTEHM.2023.3334238","url":null,"abstract":"This work aims to explore the utility of wearable inertial measurement units (IMUs) for quantifying movement in Romberg tests and investigate the extent of movement in adults with vestibular hypofunction (VH). A cross-sectional study was conducted at an academic tertiary medical center between March 2021 and April 2022. Adults diagnosed with unilateral vestibular hypofunction (UVH) or bilateral vestibular hypofunction (BVH) were enrolled in the VH group. Healthy controls (HCs) were recruited from community or outpatient clinics. The IMU-based instrumented Romberg and tandem Romberg tests on the floor were applied to both groups. The primary outcomes were kinematic body metrics (maximum acceleration [ACC], mean ACC, root mean square [RMS] of ACC, and mean sway velocity [MV]) along the medio-lateral (ML), cranio-caudal (CC), and antero-posterior (AP) axes. A total of 31 VH participants (mean age, 33.48 [SD 7.68] years; 19 [61%] female) and 31 HCs (mean age, 30.65 [SD 5.89] years; 18 [58%] female) were recruited. During the eyes-closed portion of the Romberg test, VH participants demonstrated significantly higher maximum ACC and increased RMS of ACC in head movement, as well as higher maximum ACC in pelvic movement along the ML axis. In the same test condition, individuals with BVH exhibited notably higher maximum ACC and RMS of ACC along the ML axis in head and pelvic movements compared with HCs. Additionally, BVH participants exhibited markedly increased maximum ACC along the ML axis in head movement during the eyes-open portion of the tandem Romberg test. Conversely, no significant differences were found between UVH participants and HCs in the assessed parameters. The instrumented Romberg and tandem Romberg tests characterized the kinematic differences in head, pelvis, and ankle movement between VH and healthy adults. The findings suggest that these kinematic body metrics can be useful for screening BVH and can provide goals for vestibular rehabilitation.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"245-255"},"PeriodicalIF":3.4,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10322744","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034303","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 accuracy of screw placement is a key factor for the stability of the cannulated screws used in the fixation of femoral neck fractures. In this study we designed a navigation device for ensuring the screw reaches the ideal position for optimal fixation. From March 2019 to September 2020, 66 patients with femoral neck fracture were enrolled and divided into 2 groups, one group was treated using the traditional free-hand cannulated screw fixation and the other using the new navigation device with assisted fixation. The effectiveness of the 2 methods was compared based on surgery duration, intraoperative bleeding, number of fluoroscopic examination and guidewire insertion attempts, screw parallelism, and effective fixation area. Fracture healing, complications and hip joint function were assessed after operation. The new navigation device reduced the duration of surgery without causing additional intraoperative bleeding, and significantly reduced number of fluoroscopy examination and guidewire insertion attempts (4.00±1.58 vs. 6.09±1.94 with traditional surgery). The accuracy of screw implantation was improved, as demonstrated by increased screw parallelism (0.71±0.57° vs. 1.66 ±1.01° with traditional surgery) and higher effective fixed area (64.88±10.52 vs. 58.61±9.19 mm2 with traditional surgery). In the postoperative follow-up, except for one case of femoral head necrosis and one case of bone nonunion in the traditional surgical group, the other patients showed fracture healing. There was no significant difference in hip joint function between the 2 groups. The new navigation device enables rapid and accurate guidewire positioning for cannulated screw fixation through simple operation procedures, resulting in good prospect for clinical transformation.
{"title":"A Novel Navigation Device for Precise Percutaneous Placement of the Guidewire in Femoral Neck Fracture Cannulated Screw Fixation Surgery","authors":"Yutao Cui;Guangkai Ren;Chuangang Peng;Baoming Yuan;Dankai Wu","doi":"10.1109/JTEHM.2023.3332453","DOIUrl":"https://doi.org/10.1109/JTEHM.2023.3332453","url":null,"abstract":"The accuracy of screw placement is a key factor for the stability of the cannulated screws used in the fixation of femoral neck fractures. In this study we designed a navigation device for ensuring the screw reaches the ideal position for optimal fixation. From March 2019 to September 2020, 66 patients with femoral neck fracture were enrolled and divided into 2 groups, one group was treated using the traditional free-hand cannulated screw fixation and the other using the new navigation device with assisted fixation. The effectiveness of the 2 methods was compared based on surgery duration, intraoperative bleeding, number of fluoroscopic examination and guidewire insertion attempts, screw parallelism, and effective fixation area. Fracture healing, complications and hip joint function were assessed after operation. The new navigation device reduced the duration of surgery without causing additional intraoperative bleeding, and significantly reduced number of fluoroscopy examination and guidewire insertion attempts (4.00±1.58 vs. 6.09±1.94 with traditional surgery). The accuracy of screw implantation was improved, as demonstrated by increased screw parallelism (0.71±0.57° vs. 1.66 ±1.01° with traditional surgery) and higher effective fixed area (64.88±10.52 vs. 58.61±9.19 mm2 with traditional surgery). In the postoperative follow-up, except for one case of femoral head necrosis and one case of bone nonunion in the traditional surgical group, the other patients showed fracture healing. There was no significant difference in hip joint function between the 2 groups. The new navigation device enables rapid and accurate guidewire positioning for cannulated screw fixation through simple operation procedures, resulting in good prospect for clinical transformation.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"162-170"},"PeriodicalIF":3.4,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10319461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138485009","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 : 2023-11-14DOI: 10.1109/JTEHM.2023.3332618
Jason Wong;Marek Reformat;Edmond Lou
Objective: Measuring the severity of the lateral spinal curvature, or Cobb angle, is critical for monitoring and making treatment decisions for children with adolescent idiopathic scoliosis (AIS). However, manual measurement is time-consuming and subject to human error. Therefore, clinicians seek an automated measurement method to streamline workflow and improve accuracy. This paper reports on a novel machine learning algorithm of cascaded convolutional neural networks (CNN) to measure the Cobb angle on spinal radiographs automatically. Methods: The developed method consisted of spinal column segmentation using a CNN, vertebra localization and segmentation using iterative vertebra body location coupled with another CNN, point-set registration to correct vertebra segmentations, and Cobb angle measurement using the final segmentations. Measurement performance was evaluated with the circular mean absolute error (CMAE) and percentage within clinical acceptance ( $le 5^{circ }$ ) between automatic and manual measurements. Analysis was separated by curve severity to identify any potential systematic biases using independent samples Student’s t-tests. Results: The method detected 346 of the 352 manually measured Cobb angles (98%), with a CMAE of 2.8° and 91% of measurements within the 5° clinical acceptance. No statistically significant differences were found between the CMAEs of mild ( $ < 25^{circ }$ ), moderate (25°-45°), and severe ( $ge 45^{circ }$ ) groups. The average measurement time per radiograph was 17.7±10.2s, improving upon the estimated average of 30s it takes an experienced rater to measure. Additionally, the algorithm outputs segmentations with the measurement, allowing clinicians to interpret measurement results. Discussion/Conclusion: The developed method measured Cobb angles on radiographs automatically with high accuracy, quick measurement time, and interpretability, suggesting clinical feasibility.
目的:测量侧侧脊柱弯曲或Cobb角的严重程度对于监测和制定青少年特发性脊柱侧凸(AIS)的治疗决策至关重要。然而，手动测量非常耗时，而且容易出现人为错误。因此，临床医生寻求一种自动化的测量方法来简化工作流程并提高准确性。本文报道了一种新的级联卷积神经网络(CNN)机器学习算法，用于自动测量脊柱x线片上的Cobb角。方法:所开发的方法包括使用CNN进行脊柱分割，使用迭代椎体定位与另一个CNN进行椎体定位和分割，使用点集配准校正椎体分割，以及使用最终分割的Cobb角测量。采用自动测量和手动测量的循环平均绝对误差(CMAE)和临床可接受百分比($le 5^{circ }$)来评估测量性能。分析以曲线严重程度分开，使用独立样本学生t检验来识别任何潜在的系统偏差。结果:该方法检测出352个人工测得的Cobb角中的346个(98%), with a CMAE of 2.8° and 91% of measurements within the 5° clinical acceptance. No statistically significant differences were found between the CMAEs of mild ( $ < 25^{circ }$ ), moderate (25°-45°), and severe ( $ge 45^{circ }$ ) groups. The average measurement time per radiograph was 17.7±10.2s, improving upon the estimated average of 30s it takes an experienced rater to measure. Additionally, the algorithm outputs segmentations with the measurement, allowing clinicians to interpret measurement results. Discussion/Conclusion: The developed method measured Cobb angles on radiographs automatically with high accuracy, quick measurement time, and interpretability, suggesting clinical feasibility.
{"title":"Applying Machine Learning and Point-Set Registration to Automatically Measure the Severity of Spinal Curvature on Radiographs","authors":"Jason Wong;Marek Reformat;Edmond Lou","doi":"10.1109/JTEHM.2023.3332618","DOIUrl":"10.1109/JTEHM.2023.3332618","url":null,"abstract":"Objective: Measuring the severity of the lateral spinal curvature, or Cobb angle, is critical for monitoring and making treatment decisions for children with adolescent idiopathic scoliosis (AIS). However, manual measurement is time-consuming and subject to human error. Therefore, clinicians seek an automated measurement method to streamline workflow and improve accuracy. This paper reports on a novel machine learning algorithm of cascaded convolutional neural networks (CNN) to measure the Cobb angle on spinal radiographs automatically. Methods: The developed method consisted of spinal column segmentation using a CNN, vertebra localization and segmentation using iterative vertebra body location coupled with another CNN, point-set registration to correct vertebra segmentations, and Cobb angle measurement using the final segmentations. Measurement performance was evaluated with the circular mean absolute error (CMAE) and percentage within clinical acceptance (\u0000<inline-formula> <tex-math>$le 5^{circ }$ </tex-math></inline-formula>\u0000) between automatic and manual measurements. Analysis was separated by curve severity to identify any potential systematic biases using independent samples Student’s t-tests. Results: The method detected 346 of the 352 manually measured Cobb angles (98%), with a CMAE of 2.8° and 91% of measurements within the 5° clinical acceptance. No statistically significant differences were found between the CMAEs of mild (\u0000<inline-formula> <tex-math>$ < 25^{circ }$ </tex-math></inline-formula>\u0000), moderate (25°-45°), and severe (\u0000<inline-formula> <tex-math>$ge 45^{circ }$ </tex-math></inline-formula>\u0000) groups. The average measurement time per radiograph was 17.7±10.2s, improving upon the estimated average of 30s it takes an experienced rater to measure. Additionally, the algorithm outputs segmentations with the measurement, allowing clinicians to interpret measurement results. Discussion/Conclusion: The developed method measured Cobb angles on radiographs automatically with high accuracy, quick measurement time, and interpretability, suggesting clinical feasibility.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"151-161"},"PeriodicalIF":3.4,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10318103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135704997","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 : 2023-11-13DOI: 10.1109/JTEHM.2023.3332088
Sara Condino;Fabrizio Cutolo;Marina Carbone;Laura Cercenelli;Giovanni Badiali;Nicola Montemurro;Vincenzo Ferrari
Achieving and maintaining proper image registration accuracy is an open challenge of image-guided surgery. This work explores and assesses the efficacy of a registration sanity check method for augmented reality-guided navigation (AR-RSC), based on the visual inspection of virtual 3D models of landmarks. We analyze the AR-RSC sensitivity and specificity by recruiting 36 subjects to assess the registration accuracy of a set of 114 AR images generated from camera images acquired during an AR-guided orthognathic intervention. Translational or rotational errors of known magnitude up to ±1.5 mm/±15.5°, were artificially added to the image set in order to simulate different registration errors. This study analyses the performance of AR-RSC when varying (1) the virtual models selected for misalignment evaluation (e. g., the model of brackets, incisor teeth, and gingival margins in our experiment), (2) the type (translation/rotation) of registration error, and (3) the level of user experience in using AR technologies. Results show that: 1) the sensitivity and specificity of the AR-RSC depends on the virtual models (globally, a median true positive rate of up to 79.2% was reached with brackets, and a median true negative rate of up to 64.3% with incisor teeth), 2) there are error components that are more difficult to identify visually, 3) the level of user experience does not affect the method. In conclusion, the proposed AR-RSC, tested also in the operating room, could represent an efficient method to monitor and optimize the registration accuracy during the intervention, but special attention should be paid to the selection of the AR data chosen for the visual inspection of the registration accuracy.
实现并保持适当的图像配准精度是图像引导手术的一项公开挑战。这项研究基于对虚拟三维地标模型的视觉检查，探索并评估了增强现实导航（AR-RSC）的配准正确性检查方法的有效性。我们通过招募 36 名受试者来分析 AR-RSC 的灵敏度和特异性，以评估一组 114 幅 AR 图像的配准准确性，这些图像是在 AR 指导的正颌干预过程中获取的相机图像生成的。为了模拟不同的配准误差，人为地在图像集中添加了已知幅度的平移或旋转误差，最大误差为±1.5毫米/±15.5°。本研究分析了 AR-RSC 在不同情况下的性能：(1) 用于错位评估的虚拟模型（例如，我们实验中的托槽、门牙和龈缘模型）；(2) 套准误差的类型（平移/旋转）；(3) 用户使用 AR 技术的经验水平。结果显示1）AR-RSC 的灵敏度和特异性取决于虚拟模型（在全球范围内，托槽的真阳性率中位数高达 79.2%，门牙的真阴性率中位数高达 64.3%），2）有些误差成分更难通过视觉识别，3）用户经验水平不会影响该方法。总之，建议的 AR-RSC 也在手术室中进行了测试，可以作为在干预过程中监控和优化套准准确性的有效方法，但应特别注意选择用于目测套准准确性的 AR 数据。
{"title":"Registration Sanity Check for AR-guided Surgical Interventions: Experience From Head and Face Surgery","authors":"Sara Condino;Fabrizio Cutolo;Marina Carbone;Laura Cercenelli;Giovanni Badiali;Nicola Montemurro;Vincenzo Ferrari","doi":"10.1109/JTEHM.2023.3332088","DOIUrl":"10.1109/JTEHM.2023.3332088","url":null,"abstract":"Achieving and maintaining proper image registration accuracy is an open challenge of image-guided surgery. This work explores and assesses the efficacy of a registration sanity check method for augmented reality-guided navigation (AR-RSC), based on the visual inspection of virtual 3D models of landmarks. We analyze the AR-RSC sensitivity and specificity by recruiting 36 subjects to assess the registration accuracy of a set of 114 AR images generated from camera images acquired during an AR-guided orthognathic intervention. Translational or rotational errors of known magnitude up to ±1.5 mm/±15.5°, were artificially added to the image set in order to simulate different registration errors. This study analyses the performance of AR-RSC when varying (1) the virtual models selected for misalignment evaluation (e. g., the model of brackets, incisor teeth, and gingival margins in our experiment), (2) the type (translation/rotation) of registration error, and (3) the level of user experience in using AR technologies. Results show that: 1) the sensitivity and specificity of the AR-RSC depends on the virtual models (globally, a median true positive rate of up to 79.2% was reached with brackets, and a median true negative rate of up to 64.3% with incisor teeth), 2) there are error components that are more difficult to identify visually, 3) the level of user experience does not affect the method. In conclusion, the proposed AR-RSC, tested also in the operating room, could represent an efficient method to monitor and optimize the registration accuracy during the intervention, but special attention should be paid to the selection of the AR data chosen for the visual inspection of the registration accuracy.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"258-267"},"PeriodicalIF":3.4,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10315237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135659363","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 : 2023-11-09DOI: 10.1109/JTEHM.2023.3331977
Alessandro Di Nuovo
The rapid advancement of Artificial Intelligence (AI) is transforming healthcare and daily life, offering great opportunities but also posing ethical and societal challenges. To ensure AI benefits all individuals, including those with intellectual disabilities, the focus should be on adaptive technology that can adapt to the unique needs of the user. Biomedical engineers have an interdisciplinary background that helps them to lead multidisciplinary teams in the development of human-centered AI solutions. These solutions can personalize learning, enhance communication, and improve accessibility for individuals with intellectual disabilities. Furthermore, AI can aid in healthcare research, diagnostics, and therapy. The ethical use of AI in healthcare and the collaboration of AI with human expertise must be emphasized. Public funding for inclusive research is encouraged, promoting equity and economic growth while empowering those with intellectual disabilities in society.
{"title":"Letter to the Editor: “How Can Biomedical Engineers Help Empower Individuals With Intellectual Disabilities? The Potential Benefits and Challenges of AI Technologies to Support Inclusivity and Transform Lives”","authors":"Alessandro Di Nuovo","doi":"10.1109/JTEHM.2023.3331977","DOIUrl":"10.1109/JTEHM.2023.3331977","url":null,"abstract":"The rapid advancement of Artificial Intelligence (AI) is transforming healthcare and daily life, offering great opportunities but also posing ethical and societal challenges. To ensure AI benefits all individuals, including those with intellectual disabilities, the focus should be on adaptive technology that can adapt to the unique needs of the user. Biomedical engineers have an interdisciplinary background that helps them to lead multidisciplinary teams in the development of human-centered AI solutions. These solutions can personalize learning, enhance communication, and improve accessibility for individuals with intellectual disabilities. Furthermore, AI can aid in healthcare research, diagnostics, and therapy. The ethical use of AI in healthcare and the collaboration of AI with human expertise must be emphasized. Public funding for inclusive research is encouraged, promoting equity and economic growth while empowering those with intellectual disabilities in society.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"256-257"},"PeriodicalIF":3.4,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10314515","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562888","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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