Intelligence-based medicine最新文献

{"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":"Advancing drug discovery and development through GPT models: a review on challenges, innovations and future prospects","authors":"Zhinya Kawa Othman ,&nbsp;Mohamed Mustaf Ahmed ,&nbsp;Olalekan John Okesanya ,&nbsp;Adamu Muhammad Ibrahim ,&nbsp;Shuaibu Saidu Musa ,&nbsp;Bryar A. Hassan ,&nbsp;Lanja Ibrahim Saeed ,&nbsp;Don Eliseo Lucero-Prisno III","doi":"10.1016/j.ibmed.2025.100233","DOIUrl":"10.1016/j.ibmed.2025.100233","url":null,"abstract":"<div><div>Advanced AI algorithms, notably generative pre-trained transformer (GPT) models, are revolutionizing healthcare and drug discovery and development by efficiently processing and interpreting large volumes of medical data. Specialized models, such as ProtGPT2 and BioGPT, extend their capabilities to protein engineering and biomedical text mining. Our study will contribute to ongoing discussions to revolutionize drug development, leading to a faster and more reliable validation of new therapeutic agents that are crucial for healthcare advancement and patient outcomes. GPT models, such as MTMol-GPT, are robust, generalizable, and provide important information for developing treatments for complicated disorders. SynerGPT utilizes a genetic algorithm to optimize prompts and select drug combinations for testing based on individual patient characteristics. Ligand generation for specific target proteins with potential drug activity is a significant stage in the drug design process, which enhances the quality of the synthesized compounds and augments the precision of capturing chemical structures and their activity correlations, highlighting the model's creativity and capability for innovative ligand design. Despite these advancements, there are still problems with the data volume, scalability, interpretability, and validation. Ethical considerations, robust methods, and omics data must be successfully integrated to develop AI for drug discovery and ensure successful deployment. In summary, these models significantly influence drug research and development, specifically in the earlier stages from initial target selection to post-marketing surveillance for medication safety monitoring.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548273","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":"Testing the real-world utility of Bayes theorem in artificial intelligence-enabled electrocardiogram algorithm for the detection of left ventricular systolic dysfunction","authors":"Betsy J. Medina-Inojosa ,&nbsp;David M. Harmon ,&nbsp;Jose R. Medina-Inojosa ,&nbsp;Rickey E. Carter ,&nbsp;Itzhak Zachi Attia ,&nbsp;Paul A. Friedman ,&nbsp;Francisco Lopez-Jimenez","doi":"10.1016/j.ibmed.2025.100238","DOIUrl":"10.1016/j.ibmed.2025.100238","url":null,"abstract":"<div><h3>Objective</h3><div>To assess how the theoretical principles of Bayes' theorem hold true in a clinically impactful way when testing the diagnostic performance of an artificial intelligence (AI) tool, using the case of the AI-enabled electrocardiogram (AI-ECG) screening tool that detects left ventricular systolic dysfunction (LVSD) in a “real-world” setting.</div></div><div><h3>Patient and methods</h3><div>We analyzed data from 42,883 consecutive patients who underwent a clinically indicated ECG and an echocardiogram within two weeks at our center between January 1st and December 31st^, 2019. We then evaluated area under the curve (AUC) of the receiver operating characteristics, sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the AI-ECG to detect LVSD (left ventricle ejection fraction of ≤40 %) across (i) cumulative risk factor prevalence (pre-test probabilities) (ii) different diagnostic thresholds, using paired ECG-echocardiogram data.</div></div><div><h3>Results</h3><div>Prevalence of LVSD was 1.9 %, 4.0 %, 7.0 % and 13.9 % for patients with 0, 1–2, 3–4 and ≥5 risk-factors for LVSD. The AUC of the AI-ECG for each group was 0.955, 0.933, 0.901 and 0.886, respectively (p for trend&lt;0.001). Pre-test probabilities hardly influenced sensitivity but did impact specificity. PPV was affected more than NPV, which was modestly altered. Thresholds impacted diagnostic performance parameters, although their effect on NPV at low pre-test probability was negligible.</div></div><div><h3>Conclusion</h3><div>In real world, pre-test probabilities/cumulative risk-factors of disease do affect specificity. Using different diagnostic thresholds yields the highest impact on algorithm performance. A Bayesian approach may enhance individualized diagnostic performance when implementing AI algorithms.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100238"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825511","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":"Advancing breast cancer detection in ultrasound images using a novel hybrid ensemble deep learning model","authors":"Radwan Qasrawi ,&nbsp;Omar Daraghmeh ,&nbsp;Suliman Thwib ,&nbsp;Ibrahem Qdaih ,&nbsp;Ghada Issa ,&nbsp;Stephanny Vicuna Polo ,&nbsp;Haneen Owienah ,&nbsp;Diala Abu Al-Halawa ,&nbsp;Siham Atari","doi":"10.1016/j.ibmed.2025.100222","DOIUrl":"10.1016/j.ibmed.2025.100222","url":null,"abstract":"<div><div>Breast cancer remains a leading cause of mortality among women globally, emphasizing the critical need for prompt and accurate detection to improve patient outcomes. This study introduces an innovative hybrid model combining ultrasound image enhancement techniques with advanced machine learning for rapid and more accurate breast cancer prognosis. The proposed model integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) for image quality improvement with an Ensemble Deep Random Vector Functional Link Neural Network (edRVFL) for classification. Utilizing a dataset of 4103 high-resolution ultrasound images from the Dunya Women's Cancer Center in Palestine, categorized into normal, benign, and malignant groups, the model was trained and evaluated using a 25-fold cross-validation approach. Results demonstrate higher performance of the hybrid model compared to traditional machine learning algorithms, achieving accuracies of 96 % for benign and 98 % for malignant cases after CLAHE enhancement. To further improve lesion detection and segmentation, a new method combining YOLOv5 object detection with the MedSAM foundation model was developed, achieving a Dice Similarity Coefficient of 0.988 after CLAHE enhancement. Validation in a clinical setting on 850 cases showed promising results, with 91.4 % ± 0.021 accuracy for benign and 84 % ± 0.024 for malignant predictions compared to histopathology. The model's high accuracy and interpretability, supported by Grad-CAM analysis, demonstrate its potential for integration into clinical practice. This study advances the application of machine learning in breast cancer detection from ultrasound images, presenting a valuable tool for enabling early detection and improving prognosis for breast cancer patients.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100222"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can artificial intelligence help physicians using diaphragmatic ultrasound?","authors":"Tianjie Zhang ,&nbsp;Changchun Li ,&nbsp;Dongwei Xu ,&nbsp;Yan Liu ,&nbsp;Qi Zhang ,&nbsp;Ye Song","doi":"10.1016/j.ibmed.2025.100202","DOIUrl":"10.1016/j.ibmed.2025.100202","url":null,"abstract":"<div><h3>Purpose</h3><div>We investigated the role of artificially intelligent architecture based on deep learning radiomics (DLR) in analyzing M-mode and B-mode ultrasound videos of the diaphragm for diaphragmatic ultrasound.</div></div><div><h3>Methods</h3><div>A total of 196 subjects underwent pulmonary function and ultrasonic examination of the diaphragm. All diaphragmatic ultrasound videos were collected by experienced sonographers as the entire dataset used in this study. The experiment was partitioned into two parts. First, the diaphragm images (including M-mode and B-mode) of 157 subjects were input into the artificial intelligence architecture by the AI team. Second, the test set comprised 39 subjects, each equipped with three mobility images and three thickness images. We applied the proposed parameter calculation method to this set. The method entails segmenting the images, extracting the diaphragmatic motion and thickness variation curves from the segmentation results, and subsequently analyzing these curves to acquire the target parameters. Concurrently, we documented the time taken for each measurement. In parallel, three medical professionals performed analogue measurements. We analysed the accuracy and consistency of the artificial intelligence measurements.</div></div><div><h3>Results</h3><div>The study included a total of 196 subjects. The optimal segmentation model achieved dice scores of 73.51 % and 80.76 % on the test sets of mobility images and thickness images, respectively. Our method yielded results similar to those obtained by senior sonographers and demonstrated a high level of consistency with all three medical professionals, particularly the senior sonographer, in the measurements of diaphragm excursion (DE), diaphragm contraction duration (DCD), and diaphragmatic thickness at the end of inspiration (DTei). Meanwhile, our proposed method exhibited the highest level of time efficiency. The average duration for measuring the mobility images was 1.49s and for thickness images was 0.68s, compared to critical care physicians (8.23s, 15.89s), junior sonographers (6.14s, 9.69s), and senior sonographers (4.48s,6 0.77s).</div></div><div><h3>Conclusions</h3><div>Our study suggests that artificial intelligence can assist physicians in obtaining accurate diaphragmatic ultrasound data and reducing interobserver variability. Additionally, it could also improve time efficiency in this process.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419553","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":"V-NET-VGG16: Hybrid deep learning architecture for optimal segmentation and classification of multi-differentiated liver tumors","authors":"Amine Ben Slama ,&nbsp;Hanene Sahli ,&nbsp;Yessine Amri ,&nbsp;Salam Labidi","doi":"10.1016/j.ibmed.2025.100210","DOIUrl":"10.1016/j.ibmed.2025.100210","url":null,"abstract":"<div><div>Liver cancer is a leading cause of cancer-related mortality worldwide, underscoring the importance of early and accurate diagnosis. This study aims to develop an automatic system for liver tumor detection and classification using Computed Tomography (CT) images, addressing the critical challenge of accurately segmenting liver tumors and classifying them as benign, malignant, or normal tissues.</div><div>The proposed method combines two advanced deep learning models: V-Net for tumor segmentation and VGG16 for classification. A liver CT dataset augmented with various transformations, was used to enhance the model's robustness. The data was split into training (70 %) and testing (30 %) sets. The V-Net model performs the segmentation, isolating the liver and tumor regions from the CT images, while VGG16 is used for the classification of tumor types based on the segmented data.</div><div>The results demonstrate the effectiveness of this hybrid approach. The V-Net model achieved a Dice score of 97.34 % for accurate tumor segmentation, while the VGG16 model attained a classification accuracy of 96.52 % in differentiating between benign, malignant, and normal cases. These results surpass several existing state-of-the-art approaches in liver tumors analysis, demonstrating the potential of the proposed method for reliable and efficient medical image processing.</div><div>In conclusion, the hybrid V-Net and VGG16 architecture offers a powerful tool for the segmentation and classification of liver tumors, providing a significant improvement over manual segmentation methods that are prone to human error. This approach could aid clinicians in early diagnosis and treatment planning. Future work will focus on expanding the dataset and applying the method to other types of cancer to assess the model's generalizability and effectiveness in broader clinical settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174327","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":"A robust deep learning algorithm for lung cancer detection from computed tomography images","authors":"A.A. Abe ,&nbsp;M. Nyathi ,&nbsp;A.A. Okunade ,&nbsp;W. Pilloy ,&nbsp;B. Kgole ,&nbsp;N. Nyakale","doi":"10.1016/j.ibmed.2025.100203","DOIUrl":"10.1016/j.ibmed.2025.100203","url":null,"abstract":"<div><div>Detecting lung cancer at its earliest stage offers the best possibility for a cure. Chest computed tomography (CT) scans are a valuable tool for early diagnosis. However, the initial stages of lung cancer may present patterns in the images that are not easily detectable by radiologist, potentially leading to misdiagnosis. Although automated approaches using deep learning (DL) algorithms have been proposed, it depends on a substantial amount of data to achieve diagnostic accuracy comparable to that of radiologists. To alleviate this challenge, this study proposes a DL algorithm that uses an ensemble of convolutional neural networks and trained on relatively small dataset (IQ_OTH/NCCD dataset) to automate lung cancer diagnosis from patient chest CT scans. The method achieved an accuracy of 98.17 %, a sensitivity of 98.21 %, and a specificity of 98.13 % when categorizing scans as either cancerous or non-cancerous. Similarly, it achieved an accuracy of 95.43 %, a sensitivity of 93.40 %, and a specificity of 97.09 % when classifying scans as normal or containing benign or malignant pulmonary nodules. These results demonstrate superior performance compared to previously proposed models, highlighting the effectiveness of DL algorithms for early lung cancer diagnosis and providing a valuable tool to assist radiologists in their assesments.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174354","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}