Computers in biology and medicine最新文献

{"title":"The surface-enhanced Raman scattering method for point-of-care atrial fibrillation diagnostics","authors":"I. Boginskaya ,&nbsp;R. Safiullin ,&nbsp;V. Tikhomirova ,&nbsp;O. Kryukova ,&nbsp;K. Afanasev ,&nbsp;A. Efendieva ,&nbsp;N. Bulaeva ,&nbsp;E. Golukhova ,&nbsp;I. Ryzhikov ,&nbsp;O. Kost ,&nbsp;I. Kurochkin","doi":"10.1016/j.compbiomed.2025.109923","DOIUrl":"10.1016/j.compbiomed.2025.109923","url":null,"abstract":"<div><div>We suggest a new method for the detection of paroxysmal atrial fibrillation by analyzing surface-enhanced Raman scattering (SERS) spectra of blood serum of patients in question in comparison with SERS spectra of the serum of healthy donors. Spectral measurements were carried out on compact SERS substrates in dried blood serum droplets with immediate subsequent processing. To process the spectra, machine learning methods were used, in particular, the logistic regression method and the principal component method. Furthermore, thanks to the possibility of the physical-chemical interpretation of the coefficients of the method, the vibrational bands responsible for the signs of atrial fibrillation were identified and their correlation was carried out. Evaluation metrics were presented for the classification, among which the accuracy value was 0.82, that is a high indicator when analyzing samples directly from the blood serum of patients with the disease under study. It was shown that a small number of measured spectra for each sample (near 35 measurements) was sufficient to carry out the study. A comparative analysis of the logistic regression method and other commonly used machine learning methods was also carried out: support vector machines and random forest. Each method was evaluated and the advantages of logistic regression in solving the problem presented in this study were shown. The receiver operating characteristic curve (ROC) analysis was also used for graphical representation and comparison of methods. The presented study shows the prospects for using the described method for the analysis of diseases associated with cardiac risks.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109923"},"PeriodicalIF":7.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549229","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":"Discovery of mutations predictive of survival benefit from immunotherapy in first-line NSCLC: A retrospective machine learning study of IMpower150 liquid biopsy data","authors":"Min Yuan ,&nbsp;Wei Feng ,&nbsp;Haolun Ding ,&nbsp;Yaning Yang ,&nbsp;Xu Steven Xu","doi":"10.1016/j.compbiomed.2025.109964","DOIUrl":"10.1016/j.compbiomed.2025.109964","url":null,"abstract":"<div><div>Predictive biomarker identification in cancer treatment has traditionally relied on pre-defined analyses, limiting discoveries to expected biomarkers and potentially overlooking novel ones predictive of therapy response. In this work, we develop a novel machine-learning approach capable of exploring full landscape of mutations and combinations and identify potentially new predictive biomarkers for chemoimmunotherapy. Utilizing the liquid biopsy dataset from 313 non-small cell lung cancer (NSCLC) patients in the Phase 3 Impower150 trial (NCT02366143), we developed the HRdiffRF algorithm with a novel hazard ratio-splitting criterion. Predictive mutations and combinations were identified for overall survival (OS) improvement with atezolizumab plus bevacizumab plus carboplatin and paclitaxel (ABCP) compared to bevacizumab plus carboplatin and paclitaxel (BCP). Our analysis confirms the predictive role of <em>KRAS</em> mutations and reveals the predictive value of <em>PTPRD</em> and <em>SMARCA4</em> mutations in chemoimmunotherapy efficacy. Unlike other <em>KRAS</em> wild-type NSCLC patients, NSCLC patients with <em>KRAS</em> wild-type status and mutations in <em>FAT1</em>, <em>ERBB2</em>, or <em>PTPRD</em> may benefit from chemoimmunotherapy, while <em>NTRK3</em> and <em>GNAS</em> mutations could negatively impact survival. Patients harboring concurrent <em>KRAS</em> and <em>KEAP1</em> mutations may not benefit from chemoimmunotherapy. These findings highlight the complex genetic factors influencing treatment response for chemoimmunotherapy in NSCLC. In summary, the proposed machine-learning tool identified potential predictive biomarkers for first-line chemoimmunotherapy in NSCLC and can be readily applied to other tumor types and studies. It can also be extended to explore predictive biomarkers beyond mutations.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109964"},"PeriodicalIF":7.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549234","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":"Novel augmentation techniques using diffusion models for green wall plant health classification","authors":"MinSeok Yoon ,&nbsp;Younghoon Lee","doi":"10.1016/j.compbiomed.2025.109899","DOIUrl":"10.1016/j.compbiomed.2025.109899","url":null,"abstract":"<div><div>Green walls, vertical plant-based structures, are increasingly popular due to their diverse environmental benefits, including aesthetic enhancement, temperature and humidity regulation, and air pollutant removal. These systems, typically consisting of modular plant units, require accurate condition prediction and timely replacement of withering plants, making plant health monitoring essential for effective maintenance. Despite advancements in deep-learning-based plant classification models, real-world data collection challenges persist, particularly for the “Wilted” state, which is significantly harder to acquire than the “Normal” state. Moreover, data scarcity for the “Slightly Wilted” state critical for proactive maintenance further exacerbates the challenge. The continuous nature of plant deterioration within the “Slightly Wilted” category introduces labeling ambiguities, making accurate classification more difficult. To address these challenges, this study proposes an innovative augmentation approach that synthetically generates “Slightly Wilted” data using Diffusion Models. Specifically, the method interpolates between “Normal” and “Wilted” states through Diffusion Models, assigning soft labels based on the synthesis ratio, thereby enhancing classification model performance. Experimental results demonstrate that the proposed augmentation methodology improves classification accuracy and F1 score by up to 4% compared to models initialized with ImageNet weights, highlighting its effectiveness. Additionally, the proposed method not only classifies plant health conditions but also provides a more granular assessment of health severity, offering enhanced precision and actionable insights for green wall maintenance.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109899"},"PeriodicalIF":7.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549233","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":"Factor enhanced DeepSurv: A deep learning approach for predicting survival probabilities in cirrhosis data","authors":"Chukwudi Paul Obite ,&nbsp;Emmanuella Onyinyechi Chukwudi ,&nbsp;Merit Uchechukwu ,&nbsp;Ugochinyere Ihuoma Nwosu","doi":"10.1016/j.compbiomed.2025.109963","DOIUrl":"10.1016/j.compbiomed.2025.109963","url":null,"abstract":"<div><h3>Background</h3><div>Over the years, various models, including both traditional and machine learning models, have been employed to predict survival probabilities for diverse survival datasets. The objective is to obtain models that provide more accurate estimates of survival probabilities. Certain datasets exhibit complex nonlinear effects and interactions between variables that may necessitate the application of deep learning algorithms to comprehend the underlying data generation process.</div></div><div><h3>Method</h3><div>In this paper, we introduced Factor Enhanced DeepSurv (FE-DeepSurv), a novel deep neural network designed to study complex structures and excels at filtering noise within predictors, thereby enhancing precision of survival probability estimates. FE-DeepSurv incorporates factor analysis to reduce predictor dimensionality, applies a transformation technique to account for data censoring, and employs a deep neural network to predict conditional failure probabilities for each time interval. These predictions are subsequently utilized to estimate survival probabilities for each subject. We applied our proposed model to study cirrhosis survival data, a secondary data from Mayo Clinic trial focused on primary biliary cirrhosis (PBC) of the liver and compared its performance with the Cox proportional hazard model (Cox model), random survival forest (RSF), DeepHit, and DeepSurv, using the concordance index (C-index), brier score (BS), and integrated brier score (IBS).</div></div><div><h3>Results</h3><div>The results show that FE-DeepSurv outperforms many existing survival models. FE-DeepSurv's accurate predictions of survival probabilities and hazard rates can drive improvements in clinical practice, healthcare management, insurance risk assessment, and various other domains.</div></div><div><h3>Conclusions</h3><div>By adopting FE-DeepSurv, institutions can harness the power of advanced analytics to make more informed decisions, ultimately leading to better outcomes across multiple sectors.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109963"},"PeriodicalIF":7.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549232","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":"Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates","authors":"Jen-Fu Hsu ,&nbsp;Ying-Chih Lin ,&nbsp;Chun-Yuan Lin ,&nbsp;Shih-Ming Chu ,&nbsp;Hui-Jun Cheng ,&nbsp;Fan-Wei Xu ,&nbsp;Hsuan-Rong Huang ,&nbsp;Chen-Chu Liao ,&nbsp;Rei-Huei Fu ,&nbsp;Ming-Horng Tsai","doi":"10.1016/j.compbiomed.2025.109942","DOIUrl":"10.1016/j.compbiomed.2025.109942","url":null,"abstract":"<div><h3>Background</h3><div>Early and accurate confirmation of critically ill neonates with a suspected diagnosis of ventilator-associated pneumonia (VAP) can optimize the therapeutic strategy and avoid unnecessary use of empirical antibiotics. We aimed to examine whether deep learning (DL) methods can assist the diagnosis of VAP of intubated neonates in the neonatal intensive care unit (NICU).</div></div><div><h3>Methods</h3><div>A total of 670 neonates with mechanical ventilation were prospectively observed in a tertiary-level NICU in Taiwan between October 2017 and March 2022, during which image data were collected. All neonates with clinically suspected VAP were enrolled, and various DL methods were used to test the prediction ability of VAP diagnosis. The accuracy, precision, sensitivity, specificity, F1-score, and area under curves (AUCs) of several DL methods were compared.</div></div><div><h3>Results</h3><div>A total of 900 chest X-ray images derived from 670 neonates with VAP and/or bronchopulmonary dysplasia (BPD) were enrolled, including 399 images from patients with definite diagnosis of VAP based on the strict criteria and 501 images from neonates without VAP. Compared with conventional DNN models such as ResNet, VGG, DenseNet, the RegNetX80 achieved the best specificity of 0.8378, which facilitates a low false positive rate. For accurate diagnosis of neonatal VAP, a combinatorial model of ResNet50 and RegNetX80, created through ensemble learning, further enhanced the AUC to 0.8023 for neonates with VAP on mechanical ventilation. In addition, the consistent XAI results in the left-lower region of chest X-ray image provided informative feedback and increased confidence to AI-assisted doctors.</div></div><div><h3>Conclusions</h3><div>Deep learning methods are applicable with good predictive accuracy using chest X-ray images to help diagnosis of VAP in the NICU, which can help clinicians make decisions regarding the choices of empiric antibiotics for critically ill neonates. Future prospective trials are warranted to document its clinical usefulness and benefits on reducing medical resources.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109942"},"PeriodicalIF":7.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549231","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":"Potential of AI and ML in oncology research including diagnosis, treatment and future directions: A comprehensive prospective","authors":"Akanksha Gupta ,&nbsp;Samyak Bajaj ,&nbsp;Priyanshu Nema ,&nbsp;Arpana Purohit ,&nbsp;Varsha Kashaw ,&nbsp;Vandana Soni ,&nbsp;Sushil K. Kashaw","doi":"10.1016/j.compbiomed.2025.109918","DOIUrl":"10.1016/j.compbiomed.2025.109918","url":null,"abstract":"<div><div>Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in cancer research, offering the ability to process huge data rapidly and make precise therapeutic decisions. Over the last decade, AI, particularly deep learning (DL) and machine learning (ML), has significantly enhanced cancer prediction, diagnosis, and treatment by leveraging algorithms such as convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs). These technologies provide reliable, efficient solutions for managing aggressive diseases like cancer, which have high recurrence and mortality rates. This review prospective highlights the applications of AI in oncology, a long with FDA-approved technologies like EFAI RTSuite CT HN-Segmentation System, Quantib Prostate, and Paige Prostate, and explore their role in advancing cancer detection, personalized care, and treatment. Furthermore, we also explored broader applications of AI in healthcare, addressing challenges, limitations, regulatory considerations, and ethical implications. By presenting these advancements, we underscore AI's potential to revolutionize cancer care, management and treatment.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109918"},"PeriodicalIF":7.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529180","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":"Deep learning models for improving Parkinson's disease management regarding disease stage, motor disability and quality of life","authors":"Helena R. Gonçalves ,&nbsp;Pedro Pinheiro ,&nbsp;Cristiana Pinheiro ,&nbsp;Luís Martins ,&nbsp;Ana Margarida Rodrigues ,&nbsp;Cristina P. Santos","doi":"10.1016/j.compbiomed.2025.109961","DOIUrl":"10.1016/j.compbiomed.2025.109961","url":null,"abstract":"<div><h3>Background and objective</h3><div>Motor diagnosis, monitoring and management of Parkinson's disease (PD) focuses mainly on observational methods and, clinical scales, resulting in a subjective evaluation. Inertial sensors combined with artificial intelligence have emerged as a promising solution to help physicians perform early, differential, and objective quantification of motor symptoms over time. We hypothesize that a long short-term memory-deep neural network (LSTM) architecture could be an appropriate solution for producing three models to provide a holistic assessment of patients with PD from a single inertial sensor.</div></div><div><h3>Methods</h3><div>A custom dataset with 40 patients was created to train and test three deep learning models to classify PD disease stages, motor conditions and quality of life (QoL).</div></div><div><h3>Results</h3><div>We verified an accuracy of 89 % for the disease stage classifier, 91.7 % for the motor condition classifier, and an accuracy of 87.8 % for the QoL classifier.</div></div><div><h3>Conclusions</h3><div>We confirmed that an LSTM architecture could produce three models to improve PD management.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109961"},"PeriodicalIF":7.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529178","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":"Predictive modeling and optimization in dermatology: Machine learning for skin disease classification","authors":"Khaled Mohamad Almustafa","doi":"10.1016/j.compbiomed.2025.109946","DOIUrl":"10.1016/j.compbiomed.2025.109946","url":null,"abstract":"<div><div>The accurate diagnosis of skin diseases is crucial for effective patient management and treatment, yet traditional diagnostic methods often involve subjective interpretation and can lead to variability in outcomes. In this study, we harness the power of machine learning classifiers to enhance diagnostic accuracy by predicting skin diseases based on histopathological features extracted from biopsy samples. We evaluated the performance of six widely used classifiers: Random Forest, Logistic Regression, Stochastic Gradient Descent (SGD) Classifier, Support Vector Machine (SVM), AdaBoost, and Naive Bayes. A thorough analysis of performance metrics, including accuracy, F1-score, precision, and recall, was conducted to ascertain each model's effectiveness. Among these classifiers, the SGD Classifier stood out, achieving an exceptional accuracy of 99.09 % and an F1-score of 98.77 %, demonstrating its robustness and reliability in handling complex multi-class classification tasks. To further enhance model performance and interpretability, we employed advanced feature selection techniques, which identified the most relevant attributes influencing the predictions. Notably, features such as the Koebner phenomenon, erythema, and itching were consistently highlighted across multiple classifiers, underscoring their significance in the diagnostic process. This analysis not only emphasizes the critical role of feature selection in improving model efficiency but also facilitates a better understanding of the underlying biological mechanisms associated with skin diseases. The findings of this research provide valuable insights into the application of machine learning in dermatology, paving the way for the development of reliable and automated diagnostic tools. Future work will aim to refine feature selection methodologies, expand the dataset to enhance generalization, and explore advanced deep learning techniques to further improve classification accuracy and clinical applicability. Ultimately, this study contributes to the growing body of knowledge on the integration of machine learning in healthcare, with the potential to transform the landscape of dermatological diagnosis and patient care.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109946"},"PeriodicalIF":7.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548600","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":"Deep learning based image enhancement for dynamic non-Cartesian MRI: Application to “silent“ fMRI","authors":"Frank Riemer ,&nbsp;Marius Eldevik Rusaas ,&nbsp;Lydia Brunvoll Sandøy ,&nbsp;Florian Wiesinger ,&nbsp;Ana Beatriz Solana ,&nbsp;Lars Ersland ,&nbsp;Renate Grüner","doi":"10.1016/j.compbiomed.2025.109920","DOIUrl":"10.1016/j.compbiomed.2025.109920","url":null,"abstract":"<div><div>Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues. However, to achieve reasonable temporal resolution, under-sampled 3D radial k-space commonly results in reduced image quality. In recent years, deep learning models for improving image quality have emerged. In this study, we investigate the applicability of deep learning image enhancement methods with a focus on preserving dynamic temporal signal changes.</div><div>By utilizing high-resolution resting-state fMRI datasets from the Human Connectome Project (HCP) foundation, a ground-truth training set was constructed. The k-space trajectory coordinates of a so-called silent ‘Looping Star’ fMRI sequence was used to simulate non-Cartesian MRI data from the HCP datasets. Subsequently, these sparse resampled k-space were reconstructed, thereby generating pairs of simulated ‘Looping Star’ images and ground truth HCP images. The dataset served as the basis for training both 2D-UNet and 3D-UNet deep learning models for image enhancement. A comparative analysis was conducted, and the superior model was further fine-tuned. Evaluation of the final model's performance included standard image quality metrics as well as resting-state fMRI (rs-fMRI) analysis in the time-domain.</div><div>The 3D-UNet outperformed the 2D-UNet in the image enhancement task, resulting in a significant reduction in error between the network input and the ground truth. Specifically, the 3D-UNet achieved a 97 % reduction in the mean square error between the simulated Looping Star input and the HCP ground truth in the pre-processed dataset. Moreover, the 3D-UNet successfully preserved voxel variations, observed as the correlated activity in the posterior cingulate cortex (PCC) during rs-fMRI analysis while simultaneously mitigating noise in the time-series images.</div><div>In summary, image quality was improved and artifacts were effectively eliminated through the application of both 2D and 3D deep learning approaches. Comparative analysis of the networks indicated that the use of 3D convolutions is more advantageous than employing a deeper network with 2D convolutions, particularly in scenarios involving global artifacts. Furthermore by demonstrating that the trained neural network successfully preserved temporal characteristics in the BOLD signals, the results suggest applicability in fMRI studies.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109920"},"PeriodicalIF":7.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529174","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":"Evaluating cardiac function in ex vivo heart perfusion using lumped parameter models","authors":"Raimon Casamitjana Roig ,&nbsp;Selena S. Li ,&nbsp;Mostafa Asheghan ,&nbsp;George Olverson ,&nbsp;Doug Vincent ,&nbsp;Maya Bolger-Chen ,&nbsp;Emmanuella Ajenu ,&nbsp;Manuela Lopera Higuita ,&nbsp;Shannon N. Tessier ,&nbsp;Asishana Osho ,&nbsp;David A. D'Alessandro ,&nbsp;S. Alireza Rabi ,&nbsp;Farhad R. Nezami","doi":"10.1016/j.compbiomed.2025.109922","DOIUrl":"10.1016/j.compbiomed.2025.109922","url":null,"abstract":"<div><h3>Background</h3><div>Heart transplant outcomes and survival depend on the ability to implant well-functioning organs, but there remain no reliable, objective measures of cardiac function prior to implantation. The lack of standardized protocols and advanced technologies results in inconsistencies and subjective assessments, increasing the risk for postoperative graft dysfunction, the leading cause of short-term morbidity and mortality after transplant. Ex-vivo heart perfusion (EVHP) provides a platform to evaluate donor hearts prior to implantation, using machine perfusion to reanimate the heart to a beating, physiologic state. The FDA-approved Organ Care System (OCS) is widely utilized for the evaluation and ex vivo preservation of hearts, particularly from donors after circulatory death (DCD). However, it does not permit a physiological assessment of heart function because, while the heart continues to beat, its chambers remain devoid of perfusate and thus are unable to perform any functional work.</div></div><div><h3>Method</h3><div>In this study, we developed and validated a lumped parameter mathematical model to assess donor hearts during ex-vivo perfusion, using a customized, in-house EVHP setup that allows left ventricular loading.</div></div><div><h3>Results</h3><div>We demonstrate the ability of our mathematical model to accurately predict hemodynamic parameters, enabling performance analysis of hearts during EVHP. Our model generates pressure-volume loops, allowing for the computation of ejection fraction, and was verified with experimental measurements taken via echocardiography.</div></div><div><h3>Conclusion</h3><div>This promising tool demonstrates the unique opportunity to utilize mathematical modeling in the assessment of donor hearts, streamlining their performance evaluation. Ultimately, a more accurate assessment of donor hearts on EVHP may improve our utilization of available donor hearts, addressing the donor organ shortage that continues to limit transplant capabilities.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109922"},"PeriodicalIF":7.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529179","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}