Intelligence-based medicine最新文献

{"title":"Optimizing ResNet50 performance using stochastic gradient descent on MRI images for Alzheimer's disease classification","authors":"Mohamed Amine Mahjoubi ,&nbsp;Driss Lamrani ,&nbsp;Shawki Saleh ,&nbsp;Wassima Moutaouakil ,&nbsp;Asmae Ouhmida ,&nbsp;Soufiane Hamida ,&nbsp;Bouchaib Cherradi ,&nbsp;Abdelhadi Raihani","doi":"10.1016/j.ibmed.2025.100219","DOIUrl":"10.1016/j.ibmed.2025.100219","url":null,"abstract":"<div><div>The field of optimization is focused on the formulation, analysis, and resolution of problems involving the minimization or maximization of functions. A particular subclass of optimization problems, known as empirical risk minimization, involves fitting a model to observed data. These problems play a central role in various areas such as machine learning, statistical modeling, and decision theory, where the objective is to find a model that best approximates underlying patterns in the data by minimizing a specified loss or risk function. In deep learning (DL) systems, various optimization algorithms are utilized with the gradient descent (GD) algorithm being one of the most significant and effective. Research studies have improved the GD algorithm and developed various successful variants, including stochastic gradient descent (SGD) with momentum, AdaGrad, RMSProp, and Adam. This article provides a comparative analysis of these stochastic gradient descent algorithms based on their accuracy, loss, and training time, as well as the loss of each algorithm in generating an optimization solution. Experiments were conducted using Transfer Learning (TL) technique based on the pre-trained ResNet50 base model for image classification, with a focus on stochastic gradient (SG) for performances optimization. The case study in this work is based on a data extract from the Alzheimer's image dataset, which contains four classes such as Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. The obtained results with the Adam and SGD momentum optimizers achieved the highest accuracy of 97.66 % and 97.58 %, respectively.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100219"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173637","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":"A mobile application LukaKu as a tool for detecting external wounds with artificial intelligence","authors":"Dessy Novita ,&nbsp;Herika Hayurani ,&nbsp;Eva Krishna Sutedja ,&nbsp;Firdaus Ryan Pratomo ,&nbsp;Achmad Dino Saputra ,&nbsp;Zahra Ramadhanti ,&nbsp;Nuryadin Abutani ,&nbsp;Muhammad Rafi Triandi ,&nbsp;Aldin Mubarok Guferol ,&nbsp;Anindya Apriliyanti Pravitasari ,&nbsp;Fajar Wira Adikusuma ,&nbsp;Atiek Rostika Noviyanti","doi":"10.1016/j.ibmed.2025.100200","DOIUrl":"10.1016/j.ibmed.2025.100200","url":null,"abstract":"<div><div>This study was conducted due to the lack of applications that can assist people intreating common external wounds. Therefore, we proposed the application of image-based detection which takes external wounds and identifies them using Artificial Intelligence namely LukaKu. In addition to detecting the type of wound that occurs, the application is expected to be able to produce first aid and medicine for each existing external wound label. The model used is YOLOv5 with various versions, namely YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. By calculating the validation data, each version has its own precision, recall, f1-score, and Mean Average Precision (mAP) values which are the comparison factors in determining the best model version, where YOLOv5l with mAP value of 0.785 is the best result and YOLOv5n with mAP value of 0.588 is the result with the lowest value. In the model development process, datasets of external injuries are needed to be used during the training process and test datasets for each existing model version. After each version of the model has been successfully built and analysed, the model with the best value is implemented in the mobile application, making it easier for users to access.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174331","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":"Image-based machine learning model as a tool for classification of [18F]PR04.MZ PET images in patients with parkinsonian syndrome","authors":"Maria Jiménez ,&nbsp;Cristian Soza-Ried ,&nbsp;Vasko Kramer ,&nbsp;Sebastian A. Ríos ,&nbsp;Arlette Haeger ,&nbsp;Carlos Juri ,&nbsp;Horacio Amaral ,&nbsp;Pedro Chana-Cuevas","doi":"10.1016/j.ibmed.2025.100232","DOIUrl":"10.1016/j.ibmed.2025.100232","url":null,"abstract":"<div><div>Parkinsonian syndrome (PS) is characterized by bradykinesia, resting tremor, rigidity, and encapsulates the clinical manifestation observed in various neurodegenerative disorders. Positron emission tomography (PET) imaging plays an important role in diagnosing PS by detecting the progressive loss of dopaminergic neurons. This study aimed to develop and compare five machine-learning models for the automatic classification of 204 [¹⁸F]PR04.MZ PET images, distinguishing between patients with PS and subjects without clinical evidence for dopaminergic deficit (SWEDD). Previously analyzed and classified by three expert blind readers into PS compatible (1) and SWEDDs (0), the dataset was processed in both two-dimensional and three-dimensional formats. Five widely used pattern recognition algorithms were trained and validated their performance. These algorithms were compared against the majority reading of expert diagnosis, considered the gold standard. Comparing the accuracy of 2D and 3D format images suggests that, without the depth dimension, a single image may overemphasize specific regions. Overall, three models outperformed with an accuracy greater than 98 %, demonstrating that machine-learning models trained with [¹⁸F]PR04.MZ PET images can provide a highly accurate and precise tool to support clinicians in automatic PET image analysis. This approach may be a first step in reducing the time required for interpretation, as well as increase certainty in the diagnostic process.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628516","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":"A comparison of techniques for predicting telehealth visit failure","authors":"Alexander J. Idarraga ,&nbsp;David F. Schneider","doi":"10.1016/j.ibmed.2025.100235","DOIUrl":"10.1016/j.ibmed.2025.100235","url":null,"abstract":"<div><h3>Objective</h3><div>Telehealth is an increasingly important method for delivering care. Health systems lack the ability to accurately predict which telehealth visits will fail due to poor connection, poor technical literacy, or other reasons. This results in wasted resources and disrupted patient care. The purpose of this study is to characterize and compare various methods for predicting telehealth visit failure, and to determine the prediction method most suited for implementation in a real-time operational setting.</div></div><div><h3>Methods</h3><div>A single-center, retrospective cohort study was conducted using data sourced from our data warehouse. Patient demographic information and data characterizing prior visit success and engagement with electronic health tools were included. Three main model types were evaluated: an existing scoring model developed by Hughes et al., a regression-based scoring model, and Machine Learning classifiers. Variables were selected for their importance and anticipated availability; Number Needed to Treat was used to demonstrate the number of interventions (e.g. pre-visit phone calls) required to improve success rates in the context of weekly patient volumes.</div></div><div><h3>Results</h3><div>217, 229 visits spanning 480 days were evaluated, of which 22,443 (10.33 %) met criteria for failure. Hughes et al.’s model applied to our data yielded an Area Under the Receiver Operating Characteristics Curve (AUC ROC) of 0.678 when predicting failure. A score-based model achieved an AUC ROC of 0.698. Logistic Regression, Random Forest, and Gradient Boosting models demonstrated AUC ROCs ranging from 0.7877 to 0.7969. A NNT of 32 was achieved if the 263 highest-risk patients were selected in a low-volume week using the RF classifier, compared to an expected NNT of 90 if the same number of patients were randomly selected.</div></div><div><h3>Conclusions</h3><div>Machine Learning classifiers demonstrated superiority over score-based methods for predicting telehealth visit failure. Prospective evaluation is required; evaluation using NNT as a metric can help to operationalize these models.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100235"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747628","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":"Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon’s Adamaoua region","authors":"Esaie Naroum ,&nbsp;Ebenezer Maka Maka ,&nbsp;Hamadjam Abboubakar ,&nbsp;Paul Dayang ,&nbsp;Appolinaire Batoure Bamana ,&nbsp;Benjamin Garga ,&nbsp;Hassana Daouda Daouda ,&nbsp;Mohsen Bakouri ,&nbsp;Ilyas Khan","doi":"10.1016/j.ibmed.2025.100220","DOIUrl":"10.1016/j.ibmed.2025.100220","url":null,"abstract":"<div><div>The Plasmodium parasite, which causes malaria is transmitted by Anopheles mosquitoes, and remains a major development barrier in Africa. This is particularly true considering the conducive environment that promotes the spread of malaria. This study examines several machine learning approaches, such as long short term memory (LSTM), random forests (RF), support vector machines (SVM), and data regularization models including Ridge, Lasso, and ElasticNet, in order to forecast the occurrence of malaria in the Adamaoua region of Cameroon. The LSTM, a recurrent neural network variant, performed the best with 76% accuracy and a low error rate (RMSE = 0.08). Statistical evidence indicates that temperatures exceeding 34 degrees halt mosquito vector reproduction, thereby slowing the spread of malaria. However, humidity increases the morbidity of the condition. The survey also identified high-risk areas in Ngaoundéré Rural and Urban and Meiganga. Between 2018 and 2022, the Adamaoua region had 20.1%, 12.3%, and 10.0% of malaria cases, respectively, in these locations. According to the estimate, the number of malaria cases in the Adamaoua region will rise gradually between 2023 and 2026, peaking in 2029 before declining in 2031.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100220"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388305","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 breast cancer diagnosis with convolutional autoencoders: Enhanced performance through modified loss functions","authors":"ArunaDevi Karuppasamy ,&nbsp;Hamza zidoum ,&nbsp;Majda Said Sultan Al-Rashdi ,&nbsp;Maiya Al-Bahri","doi":"10.1016/j.ibmed.2025.100248","DOIUrl":"10.1016/j.ibmed.2025.100248","url":null,"abstract":"<div><div>The Deep Learning (DL) has demonstrated a significant impact on a various pattern recognition applications, resulting in significant advancements in areas such as visual recognition, autonomous cars, language processing, and healthcare. Nowadays, deep learning was widely applied on the medical images to identify the diseases efficiently. Still, the use of applications in clinical settings is now limited to a small number. The main factors to this might be due to an inadequate annotated data, noises in the images and challenges related to collecting data. Our research proposed a convolutional autoencoder to classify the breast cancer tumors, using the Sultan Qaboos University Hospital(SQUH) and BreakHis datasets. The proposed model named Convolutional AutoEncoder with modified Loss Function (CAE-LF) achieved a good performance, by attaining a F1-score of 0.90, recall of 0.89, and accuracy of 91%. The results obtained are comparable to those obtained in earlier researches. Additional analyses conducted on the SQUH dataset demonstrate that it yields a good performance with an F1-score of 0.91, 0.93, 0.92, and 0.93 for 4x, 10x, 20x, and 40x magnifications, respectively. Our study highlights the potential of deep learning in analyzing medical images to classify breast tumors.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887937","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":"A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM","authors":"Sajid Naveed ,&nbsp;Mujtaba Husnain","doi":"10.1016/j.ibmed.2025.100206","DOIUrl":"10.1016/j.ibmed.2025.100206","url":null,"abstract":"<div><div>Medical experts and physicians examine the gene expression abnormality in glioblastoma (GBM) cancer patients to identify the drug response. The main objective of this research is to build a machine learning (ML) based model for improve the outcome of cancer medication to save the time and effort of medical practitioners. Developing a drug response recommendation system is our goal that uses the gene expression data of cancer cell lines to predict the response of anticancer drugs in terms of half-maximal inhibitory concentration (IC50). Genetic data from a GBM cancer patient is used as input into a system to predict and recommend the response of multiple anticancer drugs in a particular cancer sample. In this research, we used K-mer molecular fragmentation to process drug SMILES in a novel way, which enabled us to build a competent model that provides drug response. We used the Light Gradient Boosting Machine (LightGBM) regression algorithm and Genomics of Drug Sensitivity of Cancer (GDSC) data for this proposed recommendation system. The results showed that all predicted IC50 values are fall within the range of the real values when examining GBM data. Two drugs, temozolomide and carmustine, were predicted with a Mean Squared Error (MSE) of 0.10 and 0.11 respectively, and 0.41 in unseen test samples. These recommended responses were then verified by expert doctors, who confirmed that the responses to these drugs were very close to the actual response. These recommendation are also effective in slowing the growth of these tumors and improving patients quality of life by monitoring medication effects.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173636","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":"Feature selection using hybridized Genghis Khan Shark with snow ablation optimization technique for multi-disease prognosis","authors":"Ruqsar Zaitoon ,&nbsp;Shaik Salma Asiya Begum ,&nbsp;Sachi Nandan Mohanty ,&nbsp;Deepa Jose","doi":"10.1016/j.ibmed.2025.100249","DOIUrl":"10.1016/j.ibmed.2025.100249","url":null,"abstract":"<div><div>The exponential growth in medical data and feature dimensionality presents significant challenges in building accurate and efficient diagnostic models. High-dimensional datasets often contain redundant or irrelevant features that degrade classification performance and increase computational burden. Feature selection (FS) is therefore a critical step in medical data analysis to enhance model accuracy and interpretability. While many recent FS techniques rely on optimization algorithms, tuning their parameters and avoiding early convergence remain major challenges. This study introduces a novel hybrid optimization technique—Hybridized Genghis Khan Shark with Snow Ablation Optimization (HyGKS-SAO)—to identify the most informative features for multi-disease classification. The raw medical datasets are first pre-processed using a Tanh-based normalization method. The HyGKS-SAO algorithm then selects optimal features, balancing exploration and exploitation effectively. Finally, a multi-kernel support vector machine (SVM) is employed to classify diseases based on the selected features. The proposed framework is evaluated on six publicly available medical datasets, including breast cancer, diabetes, heart disease, stroke, lung cancer, and chronic kidney disease. Experimental results demonstrate the effectiveness of the proposed method, achieving 98 % accuracy, 97.99 % MCC, 96.31 % PPV, 97.35 % G-mean, 98.03 % Kappa Coefficient, and a low computation time of 50 s, outperforming several state-of-the-art approaches.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848482","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":"AI speechbots and 3D segmentations in virtual reality improve radiology on-call training in resource-limited settings","authors":"Yusuf Alibrahim ,&nbsp;Muhieldean Ibrahim ,&nbsp;Devindra Gurdayal ,&nbsp;Muhammad Munshi","doi":"10.1016/j.ibmed.2025.100245","DOIUrl":"10.1016/j.ibmed.2025.100245","url":null,"abstract":"<div><h3>Objective</h3><div>Evaluate the use of large-language model (LLM) speechbot tools and deep learning-assisted generation of 3D reconstructions when integrated in a virtual reality (VR) setting to teach radiology on-call topics to radiology residents.</div></div><div><h3>Methods</h3><div>Three first year radiology residents in Guyana were enrolled in an 8-week radiology course that focused on preparation for on-call duties. The course, delivered via VR headsets with custom software integrating LLM-powered speechbots trained on imaging reports and 3D reconstructions segmented with the help of a deep learning model. Each session focused on a specific radiology area, employing a didactic and case-based learning approach, enhanced with 3D reconstructions and an LLM-powered speechbot. Post-session, residents reassessed their knowledge and provided feedback on their VR and LLM-powered speechbot experiences.</div></div><div><h3>Results/discussion</h3><div>Residents found that the 3D reconstructions segmented semi-automatically by deep learning algorithms and AI-driven self-learning via speechbot was highly valuable. The 3D reconstructions, especially in the interventional radiology session, were helpful and the benefit is augmented by VR where navigating the models is seamless and perception of depth is pronounced. Residents also found conversing with the AI-speechbot seamless and was valuable in their post session self-learning. The major drawback of VR was motion sickness, which was mild and improved over time.</div></div><div><h3>Conclusion</h3><div>AI-assisted VR radiology education could be used to develop new and accessible ways of teaching a variety of radiology topics in a seamless and cost-effective way. This could be especially useful in supporting radiology education remotely in regions which lack local radiology expertise.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100245"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747483","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":"Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation","authors":"Shujaat Ali Zaidi ,&nbsp;Varin Chouvatut ,&nbsp;Chailert Phongnarisorn ,&nbsp;Dussadee Praserttitipong","doi":"10.1016/j.ibmed.2025.100230","DOIUrl":"10.1016/j.ibmed.2025.100230","url":null,"abstract":"<div><div>Endometriosis, a complex gynecological condition, presents significant diagnostic challenges due to the subtle and varied appearance of its lesions. This study leverages deep learning to classify endometriosis lesions in laparoscopic images using the Gynecologic Laparoscopy Endometriosis Dataset (GLENDA). Three deep learning models VGG19, ResNet50, and Inception V3 were trained and evaluated with 5-fold cross-validation to enhance generalizability and mitigate overfitting. Robust data augmentation techniques were applied to address dataset limitations. The models were tasked with classifying lesions into pathological and nonpathological categories. Experimental results demonstrated strong performance, with VGG19, ResNet50, and Inception V3 achieving accuracies of 0.89, 0.91, and 0.93, respectively. Inception V3 outperformed the others, highlighting its efficacy for this task. The findings underscore the potential of deep learning in improving endometriosis diagnosis, offering a reliable tool for clinicians. This study contributes to the growing field of AI-driven medical image analysis, emphasizing the value of cross-validation and data augmentation in enhancing model performance for specialized medical applications.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100230"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621027","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}