Intelligence-based medicine最新文献

{"title":"Comparing machine learning approaches for predicting the success of ICSI treatment: A study on clinical applications","authors":"Abrar Mohammad ,&nbsp;Haneen Awad ,&nbsp;Huthaifa I. Ashqar","doi":"10.1016/j.ibmed.2025.100204","DOIUrl":"10.1016/j.ibmed.2025.100204","url":null,"abstract":"<div><div>Intracytoplasmic Sperm Injection (ICSI) is widely used to treat almost all forms of male infertility and to overcome fertilization failure. While ICSI is a powerful procedure, it's also considered quite expensive, which means couples and clinicians have to make informed decisions about whether or not to proceed with this treatment. About 10,036 patient records, 46 attribute sets, and one label column that indicates the success or failure of pregnancy after the ICSI treatment were used to conduct this research. The data were gathered from Razan infertility center in Palestine. The ICSI dataset contains only clinical features that are known prior to deciding on ICSI treatment. The dataset contains 46 features, 5 of the independent features have categorical values, 12 are numerical, 3 are string, and 26 are binary. Based on the results, RF algorithm achieved the highest AUC score of 0.97, followed by the NN with a score of 0.95, and the RIMARC algorithm with a score of 0.92. AUC is a widely used metric for evaluating the performance of binary classification models. Therefore, judging by the AUC scores, it appears that RF algorithm outperformed the other two algorithms in terms of the evaluated metric. The method employed in our analysis demonstrates considerable promise, practicality, and generalizability, driving advancements in fertility treatments and ultimately improving the chances of couples achieving their desired family goals.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100204"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174353","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":"Leveraging Conv-XGBoost algorithm for perceived mental stress detection using Photoplethysmography","authors":"Geethu S. Kumar,&nbsp;B. Ankayarkanni","doi":"10.1016/j.ibmed.2025.100209","DOIUrl":"10.1016/j.ibmed.2025.100209","url":null,"abstract":"<div><div>Stress detection is crucial for monitoring mental health and preventing stress-related disorders. Real-time stress detection shows promise with photoplethysmography (PPG), a non-invasive optical technology that analyzes blood volume changes in the microvascular bed of tissue. This study introduces a novel hybrid model, Conv-XGBoost, which combines Convolutional Neural Networks (CNN) and eXtreme Gradient Boosting (XGBoost) to improve the accuracy and robustness of stress detection from PPG signals. The Conv-XGBoost model utilizes the feature extraction capabilities of CNNs to process PPG signals, converting them into spectrograms that capture the time–frequency characteristics of data. The XGBoost component is essential for handling the complex, high-dimensional feature sets provided by the CNN, enhancing prediction capabilities through gradient boosting. This customized approach addresses the limitations of traditional machine learning algorithms in dealing with hand-crafted features. The Pulse Rate Variability-based Photoplethysmography dataset was chosen for training and validation. The outcomes of the experiments revealed that the proposed Conv-XGBoost model outperformed more conventional machine learning techniques with a training accuracy of 98.87%, validation accuracy of 93.28% and an F1-score of 97.25%. Additionally, the model demonstrated superior resilience to noise and variability in PPG signals, common in real-world scenarios. This study underscores how hybrid models can improve stress detection and sets the stage for future research integrating physiological signals with advanced deep learning techniques.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377342","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":"Predicting the prevalence of cardiovascular diseases using machine learning algorithms","authors":"Bernada E. Sianga ,&nbsp;Maurice C. Mbago ,&nbsp;Amina S. Msengwa","doi":"10.1016/j.ibmed.2025.100199","DOIUrl":"10.1016/j.ibmed.2025.100199","url":null,"abstract":"<div><div>Cardiovascular Diseases (CVDs) are the major cause of morbidity, disability, and mortality worldwide and are the most life-threatening diseases. Early detection and appropriate action can significantly reduce the effects and complications of CVD. Prediction of the likelihood that an individual can develop CVD adverse outcomes is essential. Machine learning methods are used to predict the risk of CVD incidences. Optimal model parameters were obtained using the grid search and randomized search methods. A hyperparameter tuning method with the highest accuracy was used to find the optimal parameters for the six algorithms used in this study. Two experiments were deployed: the first was training and testing the CVD dataset using hyperparameterized ML algorithms excluding geographical features, and the second included geographical features. The geographical features are air humidity, temperature and education status of a location. The performances of the two experiments were compared using classification metrics. The findings revealed that the performance of the second experiment outperformed the first experiment. XGBoost achieved the highest accuracy of 95.24 %, followed by the decision tree 93.87 % and support vector machine 92.87 % when geographical features were included (second experiment). Including geographical risk factors in predicting CVD is crucial as they contribute to the probability of developing CVD incidences.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174330","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 neonatal sepsis prediction algorithm using electronic medical record data from Mbarara Regional Referral Hospital","authors":"Peace Ezeobi Dennis ,&nbsp;Angella Musiimenta ,&nbsp;William Wasswa ,&nbsp;Stella Kyoyagala","doi":"10.1016/j.ibmed.2025.100198","DOIUrl":"10.1016/j.ibmed.2025.100198","url":null,"abstract":"<div><h3>Introduction</h3><div>Neonatal sepsis is a global challenge that contributes significantly to neonatal morbidity and mortality. The current diagnostic methods depend on conventional culture methods, a procedure that takes time and leads to delays in making timely treatment decisions. This study proposes a machine learning algorithm utilizing electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) to enhance early detection and treatment of neonatal sepsis.</div></div><div><h3>Methods</h3><div>We performed a retrospective study on a dataset of neonates hospitalized for at least 48 h in the Neonatal Intensive Care Unit (NICU) at MRRH between October 2015 to September 2019 who received at least one sepsis evaluation. 482 records of neonates met the inclusion criteria and the dataset comprises 38 neonatal sepsis screening parameters. The study considered two outcomes for sepsis evaluations: culture-positive if a blood culture was positive, and clinically positive if cultures were negative but antibiotics were administered for at least 120 h. We implemented k-fold cross-validation with k set to 10 to guarantee robust training and testing of the models. Seven machine learning models were trained to classify inputs as sepsis positive or negative, and their performance was compared with physician diagnoses.</div></div><div><h3>Results</h3><div>The results of this study show that the proposed algorithm, combining maternal risk factors, neonatal clinical signs, and laboratory tests (the algorithm demonstrated a sensitivity and specificity of at least 95 %) outperformed the physician diagnosis (Sensitivity = 89 %, Specificity = 11 %). SVM model with radial basis function, polynomial kernels, and DT model (with the highest AUROC values of 98 %) performed better than the other models.</div></div><div><h3>Conclusions</h3><div>The study shows that the combination of maternal risk factors, neonatal clinical signs, and laboratory tests can help improve the prediction of neonatal sepsis. Further research is warranted to assess the potential performance improvements and clinical efficacy in a prospective trial.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174356","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 approach to diagnose lung cancer using CT-scan images","authors":"Mohammad Q. Shatnawi,&nbsp;Qusai Abuein,&nbsp;Romesaa Al-Quraan","doi":"10.1016/j.ibmed.2024.100188","DOIUrl":"10.1016/j.ibmed.2024.100188","url":null,"abstract":"<div><div>The work in this research focuses on the automatic classification and prediction of lung cancer using computed tomography (CT) scans, employing Deep Learning (DL) strategies, specifically Enhanced Convolutional Neural Networks (CNNs), to enable rapid and accurate image analysis. This research designed and developed pre-trained models, including ConvNeXtSmall, VGG16, ResNet50, InceptionV3, and EfficientNetB0, to classify lung cancer. The dataset was divided into four classes, consisting of 338 images of adenocarcinoma, 187 images of large cell carcinoma, 260 images of squamous cell carcinoma, and 215 normal images. Notably, The Enhanced CNN model achieved an unprecedented testing accuracy of 100 %, outperforming all other models, which included ConvNeXt at 87 %, VGG16 at 99 %, ResNet50 at 94.5 %, InceptionV3 at 76.9 %, and EfficientNetB0 at 97.9 %. The study of this research is considered the first one that hits 100 % testing accuracy with an Enhanced CNN, demonstrating significant advancements in lung cancer detection through the application of sophisticated image enhancement techniques and innovative model architectures. This highlights the potential of Enhanced CNN models in transforming lung cancer diagnostics and emphasizes the importance of integrating advanced image processing techniques into clinical practice.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174358","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":"Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases","authors":"Timothy Albiges,&nbsp;Zoheir Sabeur,&nbsp;Banafshe Arbab-Zavar","doi":"10.1016/j.ibmed.2025.100217","DOIUrl":"10.1016/j.ibmed.2025.100217","url":null,"abstract":"<div><div>Chronic Obstructive Pulmonary Disease (COPD) has been presenting highly significant global health challenges for many decades. Equally, it is important to slow down this disease's ever-increasingly challenging impact on hospital patient loads. It has become necessary, if not critical, to capitalise on existing knowledge of advanced artificial intelligence to achieve the early detection of COPD and advance personalised care of COPD patients from their homes. The use of machine learning and reaching out on the classification of the multiple types of COPD severities effectively and at progressively acceptable levels of confidence is of paramount importance. Indeed, this capability will feed into highly effective personalised care of COPD patients from their homes while significantly improving their quality of life.</div><div>Auscultation lung sound analysis has emerged as a valuable, non-invasive, and cost-effective remote diagnostic tool of the future for respiratory conditions such as COPD. This research paper introduces a novel machine learning-based approach for classifying multiple COPD severities through the analysis of lung sound data streams. Leveraging two open datasets with diverse acoustic characteristics and clinical manifestations, the research study involves the transformation and decomposition of lung sound data matrices into their eigenspace representation in order to capture key features for machine learning and detection. Early eigenvalue spectra analyses were also performed to discover their distinct manifestations under the multiple established COPD severities. This has led us into projecting our experimental data matrices into their eigenspace with the use of the manifested data features prior to the machine learning process. This was followed by various methods of machine classification of COPD severities successfully. Support Vector Classifiers, Logistic Regression, Random Forests and Naive Bayes Classifiers were deployed. Systematic classifier performance metrics were also adopted; they showed early promising classification accuracies beyond 75 % for distinguishing COPD severities.</div><div>This research benchmark contributes to computer-aided medical diagnosis and supports the integration of auscultation lung sound analyses into COPD assessment protocols for individualised patient care and treatment. Future work involves the acquisition of larger volumes of lung sound data while also exploring multi-modal sensing of COPD patients for heterogeneous data fusion to advance COPD severity classification performance.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419554","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":"Artificial intelligence and patient care: Perspectives of audiologists and speech-language pathologists","authors":"Komal Aggarwal ,&nbsp;Rohit Ravi ,&nbsp;Krishna Yerraguntla","doi":"10.1016/j.ibmed.2025.100214","DOIUrl":"10.1016/j.ibmed.2025.100214","url":null,"abstract":"<div><h3>Background</h3><div>Artificial Intelligence has been implemented across various fields, including healthcare, where it has significantly advanced patient care in recent years. The present study aimed to explore the perspectives of audiologists and speech-language pathologists (ASLPs) toward AI in patient care.</div></div><div><h3>Methods</h3><div>The study employed a cross-sectional design with a convenience sampling method. The questionnaire included 27 questions consisting of demographic details and perspectives towards AI in audiology and speech language pathology services. Descriptive statistics were performed to analyze the data.</div></div><div><h3>Results</h3><div>Ninety-five ASLPs participated in the study, working across different work settings and with a mean age of 28.34 years, ranging between 18 and 47 years. Almost 50 % of participants reported AI tools can be helpful in diagnosis and planning the treatment. About One-fourth (25 %) believed that AI could help in rehabilitation. Few of participants (14.8 %) reported that AI may replace audiology and speech-language pathology services. ChatGPT was the most used platform by ASLPs in their practice. The ASLP clinicians believed AI would revolutionise ASLP practice without alarming effects on their employability.</div></div><div><h3>Conclusion</h3><div>The findings suggest that while AI has potential in ASLP practice, there is still a need for greater understanding and adoption of the technology.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173632","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":"Automatic glioma segmentation based on efficient U-net model using MRI images","authors":"Yessine Amri ,&nbsp;Amine Ben Slama ,&nbsp;Zouhair Mbarki ,&nbsp;Ridha Selmi ,&nbsp;Hedi Trabelsi","doi":"10.1016/j.ibmed.2025.100216","DOIUrl":"10.1016/j.ibmed.2025.100216","url":null,"abstract":"<div><div>Gliomas are among the most aggressive and challenging brain tumors to diagnose and treat. Accurate segmentation of glioma regions in Magnetic Resonance Imaging (MRI) is essential for early diagnosis and effective treatment planning. This study proposes an optimized U-Net model tailored for glioma segmentation, addressing key challenges such as boundary delineation, computational efficiency, and generalizability. The proposed model integrates streamlined encoder-decoder pathways and optimized skip connections, achieving precise segmentation while reducing computational complexity. The model was validated on two datasets: TCGA-TCIA, containing 110 patients, and the multi-modal BraTS 2021 dataset. Comparative evaluations were conducted against state-of-the-art methods, including Attention U-Net, Trans-U-Net, DeepLabV3+, and 3D U-Net, using metrics such as Dice Coefficient, Intersection over Union (IoU), Hausdorff Distance (HD), and Structural Similarity Index (SSIM). The proposed U-Net achieved the highest performance across all metrics, with a Dice score of 92.54 %, IoU of 90.42 %, HD of 4.12 mm, and SSIM of 0.962 on the TCGA-TCIA dataset. On the BraTS dataset, it achieved comparable results, with a Dice score of 91.32 % and an IoU of 89.56 %. In contrast, other methods, such as Attention U-Net and DeepLabV3+, showed lower Dice scores of 85.62 % and 84.10 %, respectively, and higher HD values, indicating inferior boundary delineation. Additionally, the proposed model demonstrated computational efficiency, processing images in 1.5 s on average, compared to 5.0 s for Attention U-Net and 9.0 s for Trans-U-Net. These results underscore the potential of the optimized U-Net as a robust, accurate, and efficient tool for glioma segmentation. Future work will focus on clinical validation and extending the model to include automated glioma grading, further enhancing its applicability in medical imaging workflows.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100216"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174329","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":"Advancing tuberculosis screening: A tailored CNN approach for accurate chest X-ray analysis and practical clinical integration","authors":"K.K. Mujeeb Rahman,&nbsp;Sedra Zulaikha,&nbsp;Banan Dhafer,&nbsp;Rawan Ahmed","doi":"10.1016/j.ibmed.2024.100196","DOIUrl":"10.1016/j.ibmed.2024.100196","url":null,"abstract":"<div><div>Pulmonary tuberculosis (PTB) is a chronic infectious disease claiming approximately 1.5 million lives annually, emphasizing the need for timely diagnosis to improve survival and limit its spread. Chest X-rays are effective for identifying TB-related lung abnormalities, often before symptoms arise, making early detection crucial. Our project enhances PTB screening by leveraging a CNN model trained on 12,848 images from reliable open-access datasets. The system achieves 99.72 % accuracy in binary classification (normal vs. abnormal) and 99.61 % in distinguishing healthy, TB, and non-TB cases, outperforming existing solutions. This ML-driven tool enables swift, cost-effective, and precise PTB detection, ensuring targeted treatment and addressing medicolegal needs through reliable and accountable diagnostics.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174357","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":"Skin cancer detection using deep machine learning techniques","authors":"Olusoji Akinrinade,&nbsp;Chunglin Du","doi":"10.1016/j.ibmed.2024.100191","DOIUrl":"10.1016/j.ibmed.2024.100191","url":null,"abstract":"<div><div>Technological advancements have allowed people to have unfettered access to the internet from anywhere in the world. However, there is still little access to healthcare in rural and remote areas. This study highlights the potential of deep learning techniques in improving the early detection of skin cancer, a condition affecting millions globally. By addressing the challenges of class imbalance and dataset limitations, this research presents a model that can be integrated into digital health platforms, potentially saving lives by enabling earlier diagnosis and intervention, especially in underserved regions. The study also suggest using deep learning and few-shot learning when using machine learning techniques for skin cancer diagnosis. This study utilized a novel approach the use of raw images for training and test images for test data. These input images were then pre-processed using a deep model to identify and predict subsequent outputs using the model. In addition, the effect of the Convolutional Neural Network (CNN) effect in predicting accuracy using a skin lesion's texture to differentiate between benign and malignant lesions in the body was also examined using retrieved image elements from skin photos that were significant to skin cancer identification. The study focuses on using deep learning techniques to improve the detection of skin cancer from dermoscopic images. Deep learning a top-tier method for classifying skin lesions, was applied to create an end-to-end algorithm that could identify skin cancer more accurately. A variety of deep learning backbones were utilized, addressing the challenge of class imbalance in large datasets and seeking ways to boost performance even when only small datasets are available. To overcome these obstacles, the research leveraged transfer learning, data augmentation, and Generative Adversarial Networks (GANs). It further explored different sampling techniques and loss functions that could be effective for imbalanced datasets. The study also involved a comparison between ensemble models and hybrid models to determine which was more effective for the early detection of skin cancer. The paper concluded with a discussion of the challenges faced in the early detection of skin cancer, suggesting that while progress has been made, there are still significant hurdles to overcome.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174755","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}