JMIR biomedical engineering最新文献

{"title":"Correction: Can Artificial Intelligence Diagnose Knee Osteoarthritis?","authors":"Mihir Tandon, Nitin Chetla, Adarsh Mallepally, Botan Zebari, Sai Samayamanthula, Jonathan Silva, Swapna Vaja, John Chen, Matthew Cullen, Kunal Sukhija","doi":"10.2196/82980","DOIUrl":"https://doi.org/10.2196/82980","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/67481.].</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"10 ","pages":"e82980"},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of Brachial-Ankle Pulse Wave Velocity With Hierarchical Regression Model From Wrist Photoplethysmography and Electrocardiographic Signals: Method Design.","authors":"Chih-I Ho, Chia-Hsiang Yen, Yu-Chuan Li, Chiu-Hua Huang, Jia-Wei Guo, Pei-Yun Tsai, Hung-Ju Lin, Tzung-Dau Wang","doi":"10.2196/58756","DOIUrl":"10.2196/58756","url":null,"abstract":"<p><strong>Background: </strong>Photoplethysmography (PPG) signals captured by wearable devices can provide vascular age information and support pervasive and long-term monitoring of personal health condition.</p><p><strong>Objective: </strong>In this study, we aimed to estimate brachial-ankle pulse wave velocity (baPWV) from wrist PPG and electrocardiography (ECG) from smartwatch.</p><p><strong>Methods: </strong>A total of 914 wrist PPG and ECG sequences and 278 baPWV measurements were collected via the smartwatch from 80 men and 82 women with average age of 63.4 (SD 13.4) and 64.3 (SD 11.6) years. Feature extraction and weighted pulse decomposition were applied to identify morphological characteristics regarding blood volume change and component waves in preprocessed PPG and ECG signals. A systematic strategy of feature combination was performed. The hierarchical regression method based on the random forest for classification and extreme gradient boosting (XGBoost) algorithms for regression was used, which first classified the data into subdivisions. The respective regression model for the subdivision was constructed with an overlapping zone.</p><p><strong>Results: </strong>By using 914 sets of wrist PPG and ECG signals for baPWV estimation, the hierarchical regression model with 2 subdivisions and an overlapping zone of 400 cm per second achieved root-mean-square error of 145.0 cm per second and 141.4 cm per second for 24 men and 26 women, respectively, which is better than the general XGBoost regression model and the multivariable regression model (all P<.001).</p><p><strong>Conclusions: </strong>We for the first time demonstrated that baPWV could be reliably estimated by the wrist PPG and ECG signals measured by the wearable device. Whether our algorithm could be applied clinically needs further verification.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"10 ","pages":"e58756"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Vibration for Secure Pairing With Implantable Medical Devices: Development and Usability Study.","authors":"Mo Zhang, Chaofan Wang, Weiwei Jiang, David Oswald, Toby Murray, Eduard Marin, Jing Wei, Mark Ryan, Vassilis Kostakos","doi":"10.2196/57091","DOIUrl":"10.2196/57091","url":null,"abstract":"<p><strong>Background: </strong>Implantable medical devices (IMDs), such as pacemakers, increasingly communicate wirelessly with external devices. To secure this wireless communication channel, a pairing process is needed to bootstrap a secret key between the devices. Previous work has proposed pairing approaches that often adopt a \"seamless\" design and render the pairing process imperceptible to patients. This lack of user perception can significantly compromise security and pose threats to patients.</p><p><strong>Objective: </strong>The study aimed to explore the use of highly perceptible vibrations for pairing with IMDs and aim to propose a novel technique that leverages the natural randomness in human motor behavior as a shared source of entropy for pairing, potentially deployable to current IMD products.</p><p><strong>Methods: </strong>A proof of concept was developed to demonstrate the proposed technique. A wearable prototype was built to simulate an individual acting as an IMD patient (real patients were not involved to avoid potential risks), and signal processing algorithms were devised to use accelerometer readings for facilitating secure pairing with an IMD. The technique was thoroughly evaluated in terms of accuracy, security, and usability through a lab study involving 24 participants.</p><p><strong>Results: </strong>Our proposed pairing technique achieves high pairing accuracy, with a zero false acceptance rate (indicating low risks from adversaries) and a false rejection rate of only 0.6% (1/192; suggesting that legitimate users will likely experience very few failures). Our approach also offers robust security, which passes the National Institute of Standards and Technology statistical tests (with all P values >.01). Moreover, our technique has high usability, evidenced by an average System Usability Scale questionnaire score of 73.6 (surpassing the standard benchmark of 68 for \"good usability\") and insights gathered from the interviews. Furthermore, the entire pairing process can be efficiently completed within 5 seconds.</p><p><strong>Conclusions: </strong>Vibration can be used to realize secure, usable, and deployable pairing in the context of IMDs. Our method also exhibits advantages over previous approaches, for example, lenient requirements on the sensing capabilities of IMDs and the synchronization between the IMD and the external device.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"10 ","pages":"e57091"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12379749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144982053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Pre-Existing Pain on the Body's Response to External Pain Stimuli: Experimental Study.","authors":"Burcu Ozek, Zhenyuan Lu, Srinivasan Radhakrishnan, Sagar Kamarthi","doi":"10.2196/70938","DOIUrl":"10.2196/70938","url":null,"abstract":"<p><strong>Background: </strong>Accurately assessing pain severity is essential for effective pain treatment and desirable patient outcomes. In clinical settings, pain intensity assessment relies on self-reporting methods, which are subjective to individuals and impractical for noncommunicative or critically ill patients. Previous studies have attempted to measure pain objectively using physiological responses to an external pain stimulus, assuming that the participant is free of internal body pain. However, this approach does not reflect the situation in a clinical setting, where a patient subjected to an external pain stimulus may already be experiencing internal body pain.</p><p><strong>Objective: </strong>This study investigates the hypothesis that an individual's physiological response to external pain varies in the presence of preexisting pain.</p><p><strong>Methods: </strong>We recruited 39 healthy participants aged 22-37 years, including 23 female and 16 male participants. Physiological signals, electrodermal activity, and electromyography were recorded while participants were subject to a combination of preexisting heat pain and cold pain stimuli. Feature engineering methods were applied to extract time-series features, and statistical analysis using ANOVA was conducted to assess significance.</p><p><strong>Results: </strong>We found that the preexisting pain influences the body's physiological responses to an external pain stimulus. Several features-particularly those related to temporal statistics, successive differences, and distributions-showed statistically significant variation across varying preexisting pain conditions, with P values <.05 depending on the feature and stimulus.</p><p><strong>Conclusions: </strong>Our findings suggest that preexisting pain alters the body's physiological response to new pain stimuli, highlighting the importance of considering pain history in objective pain assessment models.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"10 ","pages":"e70938"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12367283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144982068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Voice Sample Quantity and Recording Settings for the Prediction of Type 2 Diabetes Mellitus: Retrospective Study.","authors":"Atousa Assadi, Jessica Oreskovic, Jaycee Kaufman, Yan Fossat","doi":"10.2196/64357","DOIUrl":"10.2196/64357","url":null,"abstract":"<p><strong>Background: </strong>The use of acoustic biomarkers derived from speech signals is a promising non-invasive technique for diagnosing type 2 diabetes mellitus (T2DM). Despite its potential, there remains a critical gap in knowledge regarding the optimal number of voice recordings and recording schedule necessary to achieve effective diagnostic accuracy.</p><p><strong>Objective: </strong>This study aimed to determine the optimal number of voice samples and the ideal recording schedule (frequency and timing), required to maintain the T2DM diagnostic efficacy while reducing patient burden.</p><p><strong>Methods: </strong>We analyzed voice recordings from 78 adults (22 women), including 39 individuals diagnosed with T2DM. Participants had a mean (SD) age of 45.26 (10.63) years and mean (SD) BMI of 28.07 (4.59) kg/m². In total, 5035 voice recordings were collected, with a mean (SD) of 4.91 (1.45) recordings per day; higher adherence was observed among women (5.13 [1.38] vs 4.82 [1.46] in men). We evaluated the diagnostic accuracy of a previously developed voice-based model under different recording conditions. Segmented linear regression analysis was used to assess model accuracy across varying numbers of voice recordings, and the Kendall tau correlation was used to measure the relationship between recording settings and accuracy. A significance threshold of P<.05 was applied.</p><p><strong>Results: </strong>Our results showed that including up to 6 voice recordings notably improved the model accuracy for T2DM compared to using only one recording, with accuracy increasing from 59.61 to 65.02 for men and from 65.55 to 69.43 for women. Additionally, the day on which voice recordings were collected did not significantly affect model accuracy (P>.05). However, adhering to recording within a single day demonstrated higher accuracy, with accuracy of 73.95% for women and 85.48% for men when all recordings were from the first and second days.</p><p><strong>Conclusions: </strong>This study underscores the optimal voice recording settings to reduce patient burden while maintaining diagnostic efficacy.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"10 ","pages":"e64357"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144512914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Artificial Intelligence Diagnose Knee Osteoarthritis?","authors":"Mihir Tandon, Nitin Chetla, Adarsh Mallepally, Botan Zebari, Sai Samayamanthula, Jonathan Silva, Swapna Vaja, John Chen, Matthew Cullen, Kunal Sukhija","doi":"10.2196/67481","DOIUrl":"10.2196/67481","url":null,"abstract":"<p><p>This study analyzed the capability of GPT-4o to properly identify knee osteoarthritis and found that the model had good sensitivity but poor specificity in identifying knee osteoarthritis; patients and clinicians should practice caution when using GPT-4o for image analysis in knee osteoarthritis.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"10 ","pages":"e67481"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12059495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac Repair and Regeneration via Advanced Technology: Narrative Literature Review.","authors":"Yugyung Lee, Sushil Shelke, Chi Lee","doi":"10.2196/65366","DOIUrl":"10.2196/65366","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular diseases (CVDs) are the leading cause of death globally, and almost one-half of all adults in the United States have at least one form of heart disease. This review focused on advanced technologies, genetic variables in CVD, and biomaterials used for organ-independent cardiovascular repair systems.</p><p><strong>Objective: </strong>A variety of implantable and wearable devices, including biosensor-equipped cardiovascular stents and biocompatible cardiac patches, have been developed and evaluated. The incorporation of those strategies will hold a bright future in the management of CVD in advanced clinical practice.</p><p><strong>Methods: </strong>This study employed widely used academic search systems, such as Google Scholar, PubMed, and Web of Science. Recent progress in diagnostic and treatment methods against CVD, as described in the content, are extensively examined. The innovative bioengineering, gene delivery, cell biology, and artificial intelligence-based technologies that will continuously revolutionize biomedical devices for cardiovascular repair and regeneration are also discussed. The novel, balanced, contemporary, query-based method adapted in this manuscript defined the extent to which an updated literature review could efficiently provide research on the evidence-based, comprehensive applicability of cardiovascular devices for clinical treatment against CVD.</p><p><strong>Results: </strong>Advanced technologies along with artificial intelligence-based telehealth will be essential to create efficient implantable biomedical devices, including cardiovascular stents. The proper statistical approaches along with results from clinical studies including model-based risk probability prediction from genetic and physiological variables are integral for monitoring and treatment of CVD risk.</p><p><strong>Conclusions: </strong>To overcome the current obstacles in cardiac repair and regeneration and achieve successful therapeutic applications, future interdisciplinary collaborative work is essential. Novel cardiovascular devices and their targeted treatments will accomplish enhanced health care delivery and improved therapeutic efficacy against CVD. As the review articles contain comprehensive sources for state-of-the-art evidence for clinicians, these high-quality reviews will serve as a first outline of the updated progress on cardiovascular devices before undertaking clinical studies.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"10 ","pages":"e65366"},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143582415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Home Automated Telemanagement System for Individualized Exercise Programs: Design and Usability Evaluation.","authors":"Aref Smiley, Joseph Finkelstein","doi":"10.2196/65734","DOIUrl":"10.2196/65734","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Exercise is essential for physical rehabilitation, helping to improve functional performance and manage chronic conditions. Telerehabilitation offers an innovative way to deliver personalized exercise programs remotely, enhancing patient adherence and clinical outcomes. The Home Automated Telemanagement (HAT) System, integrated with the interactive bike (iBikE) system, was designed to support home-based rehabilitation by providing patients with individualized exercise programs that can be monitored remotely by a clinical rehabilitation team.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to evaluate the design, usability, and efficacy of the iBikE system within the HAT platform. We assessed the system's ability to enhance patient adherence to prescribed exercise regimens while minimizing patient and clinician burden in carrying out the rehabilitation program.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a quasi-experimental study with 5 participants using a pre- and posttest design. Usability testing included 2 primary tasks that participants performed with the iBikE system. Task completion times, adherence to exercise protocols, and user satisfaction were measured. A System Usability Scale (SUS) was also used to evaluate participants' overall experience. After an initial introduction, users performed the tasks independently following a 1-week break to assess retention of the system's operation skills and its functionality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Task completion times improved substantially from the pretest to the posttest: execution time for task 1 reduced from a mean of 8.6 (SD 4.7) seconds to a mean of 1.8 (SD 0.8) seconds, and the time for task 2 decreased from a mean of 315 (SD 6.9) seconds to a mean of 303.4 (SD 1.1) seconds. Adherence to the prescribed cycling speed also improved, with deviations from the prescribed speed reduced from a mean of 6.26 (SD 1.00) rpm (revolutions per minute) to a mean of 4.02 (SD 0.82) rpm (t=3.305, n=5, P=.03). SUS scores increased from a mean of 92 (SD 8.6) to a mean of 97 (SD 3.3), indicating high user satisfaction and confidence in system usability. All participants successfully completed both tasks without any additional assistance during the posttest phase, demonstrating the system's ease of use and effectiveness in supporting independent exercise.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The iBikE system, integrated into the HAT platform, effectively supports home-based telerehabilitation by enabling patients to follow personalized exercise prescriptions with minimal need for further training or supervision. The significant improvements in task performance and exercise adherence suggest that the system is well-suited for use in home-based rehabilitation programs, promoting sustained patient engagement and adherence to exercise regimens. Further studies with larger sample sizes are recommended to validate these findings and explore the long-term benefits of the syst","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":"e65734"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pump-Free Microfluidics for Cell Concentration Analysis on Smartphones in Clinical Settings (SmartFlow): Design, Development, and Evaluation.","authors":"Sixuan Wu, Kefan Song, Jason Cobb, Alexander T Adams","doi":"10.2196/62770","DOIUrl":"10.2196/62770","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility. Microfluidic systems, although cheaper than flow cytometers, still require high-speed cameras and syringe pumps to drive the flow and ensure video quality. In this paper, we present SmartFlow, a low-cost solution for cell concentration estimation using smartphone-based computer vision on 3D-printed, pump-free microfluidic platforms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective was to design and fabricate microfluidic chips, coupled with clinical utilities, for cell counting and concentration analysis. We answered the following research questions (RQs): RQ1, Can gravity drive the flow within the microfluidic chips, eliminating the need for external pumps? RQ2, How does the microfluidic chip design impact video quality for cell analysis? RQ3, Can smartphone-captured videos be used to estimate cell count and concentration in microfluidic chips?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;To answer the 3 RQs, 2 experiments were conducted. In the cell flow velocity experiment, diluted sheep blood flowed through the microfluidic chips with and without a bottleneck design to answer RQ1 and RQ2, respectively. In the cell concentration analysis experiment, sheep blood diluted into 13 concentrations flowed through the microfluidic chips while videos were recorded by smartphones for the concentration measurement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In the cell flow velocity experiment, we designed and fabricated 2 versions of microfluidic chips. The ANOVA test (Straight: F&lt;sub&gt;6, 99&lt;/sub&gt;=6144.45, P&lt;.001; Bottleneck: F&lt;sub&gt;6, 99&lt;/sub&gt;=3475.78, P&lt;.001) showed the height difference had a significant impact on the cell velocity, which implied gravity could drive the flow. The video sharpness analysis demonstrated that video quality followed an exponential decay with increasing height differences (video quality=100e&lt;sup&gt;-k×Height&lt;/sup&gt;) and a bottleneck design could effectively preserve video quality (Straight: R&lt;sup&gt;2&lt;/sup&gt;=0.95, k=4.33; Bottleneck: R&lt;sup&gt;2&lt;/sup&gt;=0.91, k=0.59). Samples from the 13 cell concentrations were used for cell counting and cell concentration estimation analysis. The accuracy of cell counting (n=35, 60-second samples, R&lt;sup&gt;2&lt;/sup&gt;=0.96, mean absolute error=1.10, mean squared error=2.24, root mean squared error=1.50) and cell concentration regression (n=39, 150-second samples, R&lt;sup&gt;2&lt;/sup&gt;=0.99, mean absolute error=0.24, mean squared error=0.11, root mean squared error=0.33 on a logarithmic scale, mean average percentage error=0.25) were evaluated using 5-fold cross-validation by comparing the algorithmic estimation to ground truth.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In conclusion, we demonstrated the i","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e62770"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.","authors":"Shreeram Athreya, Ashwath Radhachandran, Vedrana Ivezić, Vivek R Sant, Corey W Arnold, William Speier","doi":"10.2196/58911","DOIUrl":"10.2196/58911","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices. The challenge remains to develop a universal framework capable of improving image quality across different devices and conditions, independent of registration or specific organ characteristics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop a robust framework that enhances the quality of ultrasound images, particularly those captured with compact, portable devices, which are often constrained by low quality due to hardware limitations. The framework is designed to effectively process nonregistered ultrasound image pairs, a common challenge in medical imaging, across various clinical settings and device types. By addressing these challenges, the research seeks to provide a more generalized and adaptable solution that can be widely applied across diverse medical scenarios, improving the accessibility and quality of diagnostic imaging.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A retrospective analysis was conducted by using a cycle-consistent generative adversarial network (CycleGAN) framework enhanced with perceptual loss to improve the quality of ultrasound images, focusing on nonregistered image pairs from various organ systems. The perceptual loss was integrated to preserve anatomical integrity by comparing deep features extracted from pretrained neural networks. The model's performance was evaluated against corresponding high-resolution images, ensuring that the enhanced outputs closely mimic those from high-end ultrasound devices. The model was trained and validated using a publicly available, diverse dataset to ensure robustness and generalizability across different imaging scenarios.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The advanced CycleGAN framework, enhanced with perceptual loss, significantly outperformed the previous state-of-the-art, stable CycleGAN, in multiple evaluation metrics. Specifically, our method achieved a structural similarity index of 0.2889 versus 0.2502 (P&lt;.001), a peak signal-to-noise ratio of 15.8935 versus 14.9430 (P&lt;.001), and a learned perceptual image patch similarity score of 0.4490 versus 0.5005 (P&lt;.001). These results demonstrate the model's superior ability to enhance image quality while preserving critical anatomical details, thereby improving diagnostic usefulness.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study presents a significant advancement in ultrasound imaging by leveraging a CycleGAN model enhanced with perceptual loss to bridge the quality gap ","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"9 ","pages":"e58911"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}