Computers in biology and medicine最新文献

{"title":"Misidentifications in ayurvedic medicinal plants: Convolutional neural network (CNN) to overcome identification confusions","authors":"Nalaka Lankasena ,&nbsp;Ruwani N. Nugara ,&nbsp;Dhanesh Wisumperuma ,&nbsp;Bathiya Seneviratne ,&nbsp;Dilup Chandranimal ,&nbsp;Kamal Perera","doi":"10.1016/j.compbiomed.2024.109349","DOIUrl":"10.1016/j.compbiomed.2024.109349","url":null,"abstract":"<div><div>Plants are a vital ingredient of traditional medicine in Sri Lanka, and the quantity of medicinal plants used as a number differs in literature. Field identification of medicinal plants is carried out based on various plant characteristics. Conventional identification keys are available for plant identification, but it is a complex and time-consuming process that relies on manual observation, introducing inherent errors, particularly among individuals lacking extensive botanical expertise. This may cause uncertainty in the identification of plants due to lack of professional training, morphological similarity and nomenclatural confusion of plants. Such uncertainty may result in misidentifying another plant(s) as intended medicinal plants, which may lead to unsafe consequences. The objectives of the study were accomplished with the following three steps; listing the flowering plants used for medicinal purposes in Sri Lanka using multiple detailed botanical literatures, identifying medicinal plants that are confused with other medicinal or non-medicinal plants using literature and a questionnaire survey, and developing Convolutional Neural Networks (CNN) based technology to distinguish confusing plants. The study prepared a list of 1358 flowering plants cultivated and used in Sri Lanka as medicinal plants. Fifty-three medicinal plants that are confused with 63 medicinal and non-medicinal plant species were identified by two surveys. The CNN solution experimented with five species of the <em>Bauhinia</em> genus with close morphologically similar leaves with a high misidentification possibility. Using CNN EfficientNet-B0 Architecture for classification, four models were tested, and Model 4, which was trained using the augmented dataset with white-coloured background, resulted in a validation accuracy of 96.16%. The current study demonstrated the critical role of CNNs and EfficientNets in addressing misidentification issues in morphologically similar medicinal plants.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"183 ","pages":"Article 109349"},"PeriodicalIF":7.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579053","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}

{"title":"RADIANCE: Reliable and interpretable depression detection from speech using transformer","authors":"Anup Kumar Gupta,&nbsp;Ashutosh Dhamaniya,&nbsp;Puneet Gupta","doi":"10.1016/j.compbiomed.2024.109325","DOIUrl":"10.1016/j.compbiomed.2024.109325","url":null,"abstract":"<div><div>Depression is a common but severe mental disorder that adversely impacts the ability of an individual to function normally in their day-to-day life. A majority of depressed individuals remain undiagnosed due to factors such as social stigma and a shortage of healthcare professionals. Consequently, several Machine Learning and Deep Learning (DL) models based on speech have been proposed for automatic depression detection, with the latter generally outperforming the former. However, DL models are blackbox and offer no transparency. In contrast, healthcare professionals prefer models that provide interpretability besides being accurate. In this direction, we propose a method <em>RADIANCE</em> (Reliable AnD InterpretAble depressioN deteCtion transformErs). <em>RADIANCE</em> incorporates a novel FilterBank VIsion Transformer (<em>FBViT</em>) network, which provides the symptoms of depression as interpretable features. Additionally, we employ a novel loss function that handles the class imbalance issue in the datasets. It also incorporates a penalty term that addresses the hierarchy of misclassification errors. We also propose a reliability predictor based on low-level descriptors that provides a reliability score to indicate the trustworthiness of the prediction by <em>FBViT</em>. Furthermore, in contrast to the conventional averaging and majority pooling, <em>RADIANCE</em> consolidates predictions from multiple clips of the input audio by intricately weighing each prediction based on its reliability score, ensuring a more accurate overall prediction. <em>RADIANCE</em> outperforms the state-of-the-art depression detection methods, achieving an accuracy of 89.36%, 80.36%, and 94.44% over the DAIC-WOZ, E-DAIC, and CMDC datasets, respectively. Further, <em>RADIANCE</em> achieves MAE scores of 3.27 and 5.04 on the DAIC-WOZ and E-DAIC datasets, respectively.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"183 ","pages":"Article 109325"},"PeriodicalIF":7.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567731","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}

{"title":"FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification","authors":"Amran Hossain ,&nbsp;Rafiqul Islam ,&nbsp;Mohammad Tariqul Islam ,&nbsp;Phumin Kirawanich ,&nbsp;Mohamed S. Soliman","doi":"10.1016/j.compbiomed.2024.109316","DOIUrl":"10.1016/j.compbiomed.2024.109316","url":null,"abstract":"<div><div>The microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in their early stages. Multi-class microwave-based brain tumor (MBT) identification and classification are crucial due to the tumor's patterns and shape. Manual identification and categorization of the tumors from the images by physicians is a challenging task and consumes more time. Recently, to overcome these issues, the deep transfer learning (DTL) technique has been used to classify brain tumors efficiently. This paper proposes a Fine-tuned Feature Extracted Deep Transfer Learning Model called FT-FEDTL for multi-class MBT classification purposes. The main objective of this work is to suggest a better pathway for brain tumor diagnosis by designing an efficient DTL model that automatically identifies and categorizes the MBT images. The InceptionV3 architecture is utilized as a base for feature extraction in the proposed FT-FEDTL model. Thereafter, a fine-tuning method is applied to the additional five layers with hyperparameters. The fine-tuned layers are attached to the base model to enhance classification performance. The MBT data are collected from two sources and balanced by augmentation techniques to create a total of 4200 balanced datasets. Later, 80 % images are used for training, 20 % images are utilized for validation, and 80 samples of each class are used for testing the FT-FEDTL model for classifying tumors into six classes. We evaluated and compared the FT-FEDTL model with the three traditional non-CNN and seven pretrained models by applying an imbalanced and balanced dataset. The proposed model showed superior classification performance compared to other models for the balanced dataset. It attained an overall accuracy, recall, precision, specificity, and Fscore of 99.65 %, 99.16 %, 99.48 %, 99.10 %, and 99.23 %, respectively. The experimental outcomes ensure that the proposed model can be employed in biomedical applications to assist radiologists for multi-class MBT image classification purposes. The Anaconda distribution platform with Python 3.7 on the Windows 11 OS is used to implement the models.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"183 ","pages":"Article 109316"},"PeriodicalIF":7.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567824","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}

{"title":"Carotid single- and dual-layer stents reduce the wall adhesion of platelets by influencing flow and cellular transport","authors":"Christian J. Spieker ,&nbsp;Axelle Y. Kern ,&nbsp;Netanel Korin ,&nbsp;Pierre H. Mangin ,&nbsp;Alfons G. Hoekstra ,&nbsp;Gábor Závodszky","doi":"10.1016/j.compbiomed.2024.109313","DOIUrl":"10.1016/j.compbiomed.2024.109313","url":null,"abstract":"<div><div>An ongoing thrombosis on a ruptured atherosclerotic plaque in the carotid may cause stroke. The primary treatment for patients with tandem lesion is stenting. Dual-layer stents have been introduced as an alternative to single-layer stents for elective and emergent carotid artery stenting. While the dual-layer structure shows promise in reducing plaque prolapse through the stent struts and with it the occurrence of post-procedural embolism, there are early signs that this newer generation of stents is more thrombogenic. We investigate a single- and a dual-layer stent design to assess their influence on a set of thrombosis-related flow factors in a novel setup of combined experiments and simulations. The <em>in vitro</em> results reveal that both stents reduce thrombus formation by approximately 50% when human anticoagulated whole blood was perfused through macrofluidic flow chambers coated with either collagen or human atherosclerotic plaque homogenates. Simulations predict that the primary cause is reduced platelet presence in the vicinity of the wall, due to the influence of stents on flow and cellular transport. Both stents significantly alter the near-wall flow conditions, modifying shear rate, shear gradient, cell-free zones, and platelet availability. Additionally, the dual-layer stent has further increased local shear rates on the inner struts. It also displays increased stagnation zones and reduced recirculation between the outer-layer struts. Finally, the dual-layer stent shows further reduced adhesion over an atherosclerotic plaque coating. The novel approach presented here can be used to improve the design optimization process of cardiovascular stents in the future by allowing an in-depth study of the emerging flow characteristics and agonist transport.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"183 ","pages":"Article 109313"},"PeriodicalIF":7.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-effectiveness and cost-utility of community-based blinding fundus diseases screening with artificial intelligence: A modelling study from Shanghai, China","authors":"Senlin Lin ,&nbsp;Yingyan Ma ,&nbsp;Liping Li ,&nbsp;Yanwei Jiang ,&nbsp;Yajun Peng ,&nbsp;Tao Yu ,&nbsp;Dan Qian ,&nbsp;Yi Xu ,&nbsp;Lina Lu ,&nbsp;Yingyao Chen ,&nbsp;Haidong Zou","doi":"10.1016/j.compbiomed.2024.109329","DOIUrl":"10.1016/j.compbiomed.2024.109329","url":null,"abstract":"<div><h3>Background</h3><div>With application of artificial intelligence (AI) in the disease screening, process reengineering occurred simultaneously. Whether process reengineering deserves special emphasis in AI implementation in the community-based blinding fundus diseases screening is not clear.</div></div><div><h3>Method</h3><div>Cost-effectiveness and cost-utility analyses were performed employing decision-analytic Markov models. A hypothetical cohort of community residents was followed in the model over a period of 30 1-year Markov cycles, starting from the age of 60. The simulated cohort was based on work data of the Shanghai Digital Eye Disease Screening program (SDEDS). Three scenarios were compared: centralized screening with manual grading-based telemedicine systems (Scenario 1), centralized screening with an AI-assisted screening system (Scenario 2), and process reengineered screening with an AI-assisted screening system (Scenario 3). The main outcomes were incremental cost-effectiveness ratio (ICER) and incremental cost-utility ratio (ICUR).</div></div><div><h3>Results</h3><div>Compared with Scenario 1, Scenario 2 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $ 490010.62 per 10,000 people screened, with an ICER of $2619.98 per year of blindness avoided and an ICUR of $4589.13 per QALY. Compared with Scenario 1, Scenario 3 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $242313.23 per 10,000 people screened, with an ICER of $1295.60 per year of blindness avoided and an ICUR of $2269.35 per QALY. Although Scenario 2 and 3 could be considered cost-effective, the screening cost of Scenario 3 was 27.6 % and the total cost was 1.1 % lower, with the same expected effectiveness and utility. The probabilistic sensitivity analyses show that Scenario 3 dominated 69.1 % and 70.3 % of simulations under one and three times the local GDP per capita thresholds.</div></div><div><h3>Conclusions</h3><div>AI can improve the cost-effectiveness and cost-utility of screenings, especially when process reengineering is performed. Therefore, process reengineering is strongly recommended when AI is implemented.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"183 ","pages":"Article 109329"},"PeriodicalIF":7.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567823","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}

{"title":"Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI.","authors":"Muhammad Sami Ullah, Muhammad Attique Khan, Hussain Mobarak Albarakati, Robertas Damaševičius, Shrooq Alsenan","doi":"10.1016/j.compbiomed.2024.109183","DOIUrl":"10.1016/j.compbiomed.2024.109183","url":null,"abstract":"<p><p>Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that enable people to comprehend, properly trust, and create more explainable models. In medical imaging, XAI has been adopted to interpret deep learning black box models to demonstrate the trustworthiness of machine decisions and predictions. In this work, we proposed a deep learning and explainable AI-based framework for segmenting and classifying brain tumors. The proposed framework consists of two parts. The first part, encoder-decoder-based DeepLabv3+ architecture, is implemented with Bayesian Optimization (BO) based hyperparameter initialization. The different scales are performed, and features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique. The extracted features are passed to the output layer for tumor segmentation. In the second part of the proposed framework, two customized models have been proposed named Inverted Residual Bottleneck 96 layers (IRB-96) and Inverted Residual Bottleneck Self-Attention (IRB-Self). Both models are trained on the selected brain tumor datasets and extracted features from the global average pooling and self-attention layers. Features are fused using a serial approach, and classification is performed. The BO-based hyperparameters optimization of the neural network classifiers is performed and the classification results have been optimized. An XAI method named LIME is implemented to check the interpretability of the proposed models. The experimental process of the proposed framework was performed on the Figshare dataset, and an average segmentation accuracy of 92.68 % and classification accuracy of 95.42 % were obtained, respectively. Compared with state-of-the-art techniques, the proposed framework shows improved accuracy.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"182 ","pages":"109183"},"PeriodicalIF":7.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364647","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}

{"title":"Corrigendum to \"H2MaT-Unet:Hierarchical hybrid multi-axis transformer based Unet for medical image segmentation\" [Comput. Biol. Med. 174C (2024) 108387].","authors":"ZhiYong Ju, ZhongChen Zhou, ZiXiang Qi, Cheng Yi","doi":"10.1016/j.compbiomed.2024.109293","DOIUrl":"10.1016/j.compbiomed.2024.109293","url":null,"abstract":"","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":" ","pages":"109293"},"PeriodicalIF":7.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142459855","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}

{"title":"Beyond the root: Geometric characterization for the diagnosis of syndromic heritable thoracic aortic diseases","authors":"Pau Romero ,&nbsp;Miguel Lozano ,&nbsp;Lydia Dux-Santoy ,&nbsp;Andrea Guala ,&nbsp;Gisela Teixidó-Turà ,&nbsp;Rafael Sebastián ,&nbsp;Ignacio García-Fernández","doi":"10.1016/j.compbiomed.2024.109176","DOIUrl":"10.1016/j.compbiomed.2024.109176","url":null,"abstract":"<div><div>Syndromic heritable thoracic aortic diseases (sHTAD), such as Marfan (MFS) or Loeys–Dietz (LDS) syndromes, involve high risk of life threatening aortic events. Diagnosis of syndromic features alone is difficult, and negative genetic tests do not necessarily exclude a genetic or hereditary condition. Periodic 3D imaging of the aorta is recommended in patients with aortic disease. Thus, an imaging-based approach aimed at identifying unique features of aortic geometry can be highly effective for diagnosing sHTAD and assessing risk. In this study, we present a method that can help identify the manifestations of sHTAD by focusing on the entire geometry of the thoracic aorta, rather than only using measurements of dilation of the aortic root. We analyze the geometric phenotype of 97 patients with genetically confirmed sHTAD (79 MF and 18 LDS) and of 45 healthy volunteers, using 3D aorta meshes obtained from phase contrast-enhanced magnetic resonance angiograms computed from 4D flow cardiac magnetic resonance. We build a geometric encoding of the aorta, based on a vessel coordinate system, and use several mathematical models to discriminate between controls and patients with sHTAD: a baseline scenario, based on aortic root dimensions only, a descriptor typically used in sHTAD patients; a low dimensional scenario, with a reduce encoding using principal component analysis; and a high-dimensional scenario, which included the full coefficient representation for geometry encoding, aiming to capture finer geometric details. The results indicate that considering the anatomy of the whole thoracic aorta can improve predictive ability. We achieve precision and sensitivity values over 0.8, with a specificity of over 70% in all the models used, while a single value classifiers (based only on aortic root diameter) demonstrated a trade-off between sensitivity and specificity. Using the mathematical properties of the vessel coordinate system representation, feature importance is mapped onto a set of anatomical traits that are used by the models to do the classification, thus providing interpretability of the results. This analysis indicates that in addition to the diameter of the aortic root, aortic elongation and a narrowing of the descending thoracic aorta may be markers of positive sHTAD.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"182 ","pages":"Article 109176"},"PeriodicalIF":7.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frontoparietal atrophy trajectories in cognitively unimpaired elderly individuals using longitudinal Bayesian clustering","authors":"G. Lorenzon ,&nbsp;K. Poulakis ,&nbsp;R. Mohanty ,&nbsp;M. Kivipelto ,&nbsp;M. Eriksdotter ,&nbsp;D. Ferreira ,&nbsp;E. Westman ,&nbsp;for the Alzheimer's Disease Neuroimaging Initiative","doi":"10.1016/j.compbiomed.2024.109190","DOIUrl":"10.1016/j.compbiomed.2024.109190","url":null,"abstract":"<div><h3>Introduction</h3><div>Frontal and/or parietal atrophy has been reported during aging. To disentangle the heterogeneity previously observed, this study aimed to uncover different clusters of grey matter profiles and trajectories within cognitively unimpaired individuals.</div></div><div><h3>Methods</h3><div>Structural magnetic resonance imaging (MRI) data of 307 Aβ-negative cognitively unimpaired individuals were modelled between ages 60–85 from three cohorts worldwide. We applied unsupervised clustering using a novel longitudinal Bayesian approach and characterized the clusters' cerebrovascular and cognitive profiles.</div></div><div><h3>Results</h3><div>Four clusters were identified with different grey matter profiles and atrophy trajectories. Differences were mainly observed in frontal and parietal brain regions. These distinct frontoparietal grey matter profiles and longitudinal trajectories were differently associated with cerebrovascular burden and cognitive decline.</div></div><div><h3>Discussion</h3><div>Our findings suggest a conciliation of the frontal and parietal theories of aging, uncovering coexisting frontoparietal GM patterns. This could have important future implications for better stratification and identification of at-risk individuals.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"182 ","pages":"Article 109190"},"PeriodicalIF":7.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On-site burn severity assessment using smartphone-captured color burn wound images.","authors":"Xiayu Xu, Qilong Bu, Jingmeng Xie, Hang Li, Feng Xu, Jing Li","doi":"10.1016/j.compbiomed.2024.109171","DOIUrl":"10.1016/j.compbiomed.2024.109171","url":null,"abstract":"<p><p>Accurate assessment of burn severity is crucial for the management of burn injuries. Currently, clinicians mainly rely on visual inspection to assess burns, characterized by notable inter-observer discrepancies. In this study, we introduce an innovative analysis platform using color burn wound images for automatic burn severity assessment. To do this, we propose a novel joint-task deep learning model, which is capable of simultaneously segmenting both burn regions and body parts, the two crucial components in calculating the percentage of total body surface area (%TBSA). Asymmetric attention mechanism is introduced, allowing attention guidance from the body part segmentation task to the burn region segmentation task. A user-friendly mobile application is developed to facilitate a fast assessment of burn severity at clinical settings. The proposed framework was evaluated on a dataset comprising 1340 color burn wound images captured on-site at clinical settings. The average Dice coefficients for burn depth segmentation and body part segmentation are 85.12 % and 85.36 %, respectively. The R² for %TBSA assessment is 0.9136. The source codes for the joint-task framework and the application are released on Github (https://github.com/xjtu-mia/BurnAnalysis). The proposed platform holds the potential to be widely used at clinical settings to facilitate a fast and precise burn assessment.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"182 ","pages":"109171"},"PeriodicalIF":7.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371207","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}