Pub Date : 2025-01-01DOI: 10.1016/j.ibmed.2025.100209
Geethu S. Kumar, B. Ankayarkanni
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.
{"title":"Leveraging Conv-XGBoost algorithm for perceived mental stress detection using Photoplethysmography","authors":"Geethu S. Kumar, 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}
Pub Date : 2025-01-01DOI: 10.1016/j.ibmed.2025.100254
Nouhaila Erragzi , Nabila Zrira , Safae Lanjeri , Youssef Omor , Anwar Jimi , Ibtissam Benmiloud , Rajaa Sebihi , Rachida Latib , Nabil Ngote , Haris Ahmad Khan , Shah Nawaz
Breast cancer remains a critical health problem worldwide. Increasing survival rates requires early detection. Accurate classification and segmentation are crucial for effective diagnosis and treatment. Although breast imaging modalities offer many advantages for the diagnosis of breast cancer, the interpretation of breast ultrasound images has always been a vital issue for physicians and radiologists due to misdiagnosis. Moreover, detecting cancer at an early stage increases the chances of survival. This article presents two approaches: Attention-DenseUNet for the segmentation task and EfficientNetB7 for the classification task using public datasets: BUSI, UDIAT, BUSC, BUSIS, and STUHospital. These models are proposed in the context of Computer-Aided Diagnosis (CAD) for breast cancer detection. In the first study, we obtained an impressive Dice coefficient for all datasets, with scores of 88.93%, 95.35%, 92.79%, 93.29%, and 94.24%, respectively. In the classification task, we achieved a high accuracy using only four public datasets that include the two classes benign and malignant: BUSI, UDIAT, BUSC, and BUSIS, with an accuracy of 97%, 100%, 99%, and 94%, respectively. Generally, the results show that our proposed methods are considerably better than other state-of-the-art methods, which will undoubtedly help improve cancer diagnosis and reduce the number of false positives. Finally, we used the suggested approaches to create “Moroccan BreastCare”, an advanced breast cancer segmentation and classification software that automatically processes, segments, and classifies breast ultrasound images.
{"title":"BreastCare application: Moroccan Breast cancer diagnosis through deep learning-based image segmentation and classification","authors":"Nouhaila Erragzi , Nabila Zrira , Safae Lanjeri , Youssef Omor , Anwar Jimi , Ibtissam Benmiloud , Rajaa Sebihi , Rachida Latib , Nabil Ngote , Haris Ahmad Khan , Shah Nawaz","doi":"10.1016/j.ibmed.2025.100254","DOIUrl":"10.1016/j.ibmed.2025.100254","url":null,"abstract":"<div><div>Breast cancer remains a critical health problem worldwide. Increasing survival rates requires early detection. Accurate classification and segmentation are crucial for effective diagnosis and treatment. Although breast imaging modalities offer many advantages for the diagnosis of breast cancer, the interpretation of breast ultrasound images has always been a vital issue for physicians and radiologists due to misdiagnosis. Moreover, detecting cancer at an early stage increases the chances of survival. This article presents two approaches: Attention-DenseUNet for the segmentation task and EfficientNetB7 for the classification task using public datasets: BUSI, UDIAT, BUSC, BUSIS, and STUHospital. These models are proposed in the context of Computer-Aided Diagnosis (CAD) for breast cancer detection. In the first study, we obtained an impressive Dice coefficient for all datasets, with scores of 88.93%, 95.35%, 92.79%, 93.29%, and 94.24%, respectively. In the classification task, we achieved a high accuracy using only four public datasets that include the two classes benign and malignant: BUSI, UDIAT, BUSC, and BUSIS, with an accuracy of 97%, 100%, 99%, and 94%, respectively. Generally, the results show that our proposed methods are considerably better than other state-of-the-art methods, which will undoubtedly help improve cancer diagnosis and reduce the number of false positives. Finally, we used the suggested approaches to create “Moroccan BreastCare”, an advanced breast cancer segmentation and classification software that automatically processes, segments, and classifies breast ultrasound images.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943162","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}
Ensemble models as part of federated learning leverage the ability of individual models to learn unique patterns from the training dataset to make more efficient predictions than single predicting systems. This study aggregates the output of four best-performing EfficientNet models to arrive at the final heart failure severity prediction through federated learning. The seven variants of EfficientNet models (B0-B7) learn the features from the cardiac magnetic resonance images that are most relevant to heart failure severity. Further, the performance of every model variant has been analysed with three different optimizers i.e. Adam, SGD, and RMSprop. It has been observed that the developed ensemble prediction system provides an improved overall testing accuracy of 0.95. It is also worthy to note that the ensemble prediction has yielded significant improvement in the prediction of individual classes which is evident from sensitivity measure of 0.95, 0.88, 1.00, 0.93, and 0.98 for hyperdynamic, mild, moderate, normal and severe classes respectively. It is obvious from these results that the proposed ensemble EfficientNet prediction system would assist the radiologist in better interpretation of cardiac magnetic resonance images. This in turn would benefit the cardiologist in understanding the HF progress and planning effective therapeutic intervention.
{"title":"An intelligent ensemble EfficientNet prediction system for interpretations of cardiac magnetic resonance images in heart failure severity diagnosis","authors":"Muthunayagam Muthulakshmi , Kotteswaran Venkatesan , Balaji Prasanalakshmi , Rahayu Syarifah Bahiyah , Vijayakumar Divya","doi":"10.1016/j.ibmed.2025.100218","DOIUrl":"10.1016/j.ibmed.2025.100218","url":null,"abstract":"<div><div>Ensemble models as part of federated learning leverage the ability of individual models to learn unique patterns from the training dataset to make more efficient predictions than single predicting systems. This study aggregates the output of four best-performing EfficientNet models to arrive at the final heart failure severity prediction through federated learning. The seven variants of EfficientNet models (B0-B7) learn the features from the cardiac magnetic resonance images that are most relevant to heart failure severity. Further, the performance of every model variant has been analysed with three different optimizers i.e. Adam, SGD, and RMSprop. It has been observed that the developed ensemble prediction system provides an improved overall testing accuracy of 0.95. It is also worthy to note that the ensemble prediction has yielded significant improvement in the prediction of individual classes which is evident from sensitivity measure of 0.95, 0.88, 1.00, 0.93, and 0.98 for hyperdynamic, mild, moderate, normal and severe classes respectively. It is obvious from these results that the proposed ensemble EfficientNet prediction system would assist the radiologist in better interpretation of cardiac magnetic resonance images. This in turn would benefit the cardiologist in understanding the HF progress and planning effective therapeutic intervention.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100218"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173638","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ibmed.2025.100204
Abrar Mohammad , Haneen Awad , Huthaifa I. Ashqar
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.
{"title":"Comparing machine learning approaches for predicting the success of ICSI treatment: A study on clinical applications","authors":"Abrar Mohammad , Haneen Awad , 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}
Osteosarcoma (OS) is the most common primary bone cancer particularly in individuals aged 0–19, classified into different stages. Early diagnosis improves survival, Determination of prognosis and treatment based on it, and enables limb-sparing surgery. AI, in particular machine learning (ML) and deep learning (DL), helps analyze large datasets, identify biomarkers, predict prognosis, and personalize treatments by assessing the aforementioned features. AI has the potential to improve evaluation procedures, such as imaging and pathology approaches used in OS diagnosis, prognosis, and treatment. This study systematically examines AI's synergistic role with conventional evaluating techniques in OS treatment, improving prognostication, predicting therapy responses, and developing personalized treatment strategies.
Method
We performed an extensive search via several databases until April 23, 2024. Machine learning (ML), deep learning (DL) as the main branches of AI are often utilized in the medical sciences were searched for detection classification, and prognostication of osteosarcoma. RAYYAN.ai was used to screen the articles through the titles and abstracts. We conducted data extraction on the included articles and employed Cochrane and QUIPS tools to assess potential bias in the included non-prognosis and prognosis studies to evaluate their quality, respectively.
Results
There were 8129 articles obtained from the four databases following a thorough search. Of them 8050 ones were excluded and the remaining 78 articles published from 2013 to 2024 were reviewed. A large number of the articles indicated moderate and low risk of bias as a result of the risk of bias assessment. The majority of the articles that were reviewed (n = 48) concerned the clinical aspects of osteosarcoma; of these, 23 and 25 studies assessed diagnosis and prognoses, respectively. Furthermore, 20 articles examined image analysis specifically, 4 examined image segmentation methods, and 16 introduced classifiers to identify osteosarcoma from other diseases.
Conclusion
AI improves biomarker identification, diagnostics, and prognosis of osteosarcoma through medical imaging and data integration. Models like ResNet50 and CNN show high performance but face real-world limitations due to data heterogeneity and overfitting. This study explores AI's role in osteosarcoma diagnosis, emphasizing interdisciplinary collaboration, external validation, and real-world application challenges.
{"title":"Implementation of artificial intelligence in detection, classification, and prognostication of osteosarcoma utilizing different assessment techniques: a systematic review","authors":"Zhina Mohamadi , Paniz Partovifar , Helia Ahmadzadeh , Elmira Ali Ahmadi , Ali Ghanbari , Sina Feyzipour , Fatemeh Atefat , Nazanin Jahanpeyma , Fatemeh Haghighi asl , Armin Zarinkhat , Narges Sharbatdaran , Narges Hosseinzadeh taher , Mobina Sedighi , Fatemeh Aghajafari","doi":"10.1016/j.ibmed.2025.100250","DOIUrl":"10.1016/j.ibmed.2025.100250","url":null,"abstract":"<div><h3>Introduction</h3><div>Osteosarcoma (OS) is the most common primary bone cancer particularly in individuals aged 0–19, classified into different stages. Early diagnosis improves survival, Determination of prognosis and treatment based on it, and enables limb-sparing surgery. AI, in particular machine learning (ML) and deep learning (DL), helps analyze large datasets, identify biomarkers, predict prognosis, and personalize treatments by assessing the aforementioned features. AI has the potential to improve evaluation procedures, such as imaging and pathology approaches used in OS diagnosis, prognosis, and treatment. This study systematically examines AI's synergistic role with conventional evaluating techniques in OS treatment, improving prognostication, predicting therapy responses, and developing personalized treatment strategies.</div></div><div><h3>Method</h3><div>We performed an extensive search via several databases until April 23, 2024. Machine learning (ML), deep learning (DL) as the main branches of AI are often utilized in the medical sciences were searched for detection classification, and prognostication of osteosarcoma. RAYYAN.ai was used to screen the articles through the titles and abstracts. We conducted data extraction on the included articles and employed Cochrane and QUIPS tools to assess potential bias in the included non-prognosis and prognosis studies to evaluate their quality, respectively.</div></div><div><h3>Results</h3><div>There were 8129 articles obtained from the four databases following a thorough search. Of them 8050 ones were excluded and the remaining 78 articles published from 2013 to 2024 were reviewed. A large number of the articles indicated moderate and low risk of bias as a result of the risk of bias assessment. The majority of the articles that were reviewed (n = 48) concerned the clinical aspects of osteosarcoma; of these, 23 and 25 studies assessed diagnosis and prognoses, respectively. Furthermore, 20 articles examined image analysis specifically, 4 examined image segmentation methods, and 16 introduced classifiers to identify osteosarcoma from other diseases.</div></div><div><h3>Conclusion</h3><div>AI improves biomarker identification, diagnostics, and prognosis of osteosarcoma through medical imaging and data integration. Models like ResNet50 and CNN show high performance but face real-world limitations due to data heterogeneity and overfitting. This study explores AI's role in osteosarcoma diagnosis, emphasizing interdisciplinary collaboration, external validation, and real-world application challenges.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167924","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}
Inflammatory skin diseases often display overlapping visual features, making accurate diagnosis challenging. This study proposes a deep learning framework combining transfer learning, feature fusion, and adaptive ensemble strategies to improve dermatological image classification. Using MobileNetV3-Large as the backbone, expert-defined anatomical metadata and model-derived probabilities were fused to enrich diagnostic features. A fuzzy rank-based ensemble aggregated predictions across multiple regions of interest (ROIs), prioritizing classifier confidence dynamically. The approach achieved consistent performance across ROI settings, with F1-scores reaching 0.8. These findings demonstrate that integrating anatomical context with deep learning enhances the interpretability and diagnostic utility of automated dermatological systems.
{"title":"Skin disease classification using transfer learning model and fusion strategy","authors":"YA-Ching Yang , Wu-Chun Chung , Chun-Ying Wu , Che-Lun Hung , Yi-Ju Chen","doi":"10.1016/j.ibmed.2025.100271","DOIUrl":"10.1016/j.ibmed.2025.100271","url":null,"abstract":"<div><div>Inflammatory skin diseases often display overlapping visual features, making accurate diagnosis challenging. This study proposes a deep learning framework combining transfer learning, feature fusion, and adaptive ensemble strategies to improve dermatological image classification. Using MobileNetV3-Large as the backbone, expert-defined anatomical metadata and model-derived probabilities were fused to enrich diagnostic features. A fuzzy rank-based ensemble aggregated predictions across multiple regions of interest (ROIs), prioritizing classifier confidence dynamically. The approach achieved consistent performance across ROI settings, with F1-scores reaching 0.8. These findings demonstrate that integrating anatomical context with deep learning enhances the interpretability and diagnostic utility of automated dermatological systems.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100271"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563717","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ibmed.2025.100252
Jamilu Sani , Mohamed Mustaf Ahmed
Introduction
Timely antenatal care (ANC) initiation is essential for maternal and neonatal health, enabling the early detection of risks and ensuring optimal care. In Somalia, delayed initiation of ANC poses a significant health risk. This study applied machine learning (ML) models to predict early ANC initiation among Somali women and identify key predictors using SHapley Additive exPlanations (SHAP).
Methods
Data from the 2020 Somali Health and Demographic Survey were analyzed, focusing on ANC timing in 3138 women aged 15–49. Six ML models (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and XGBoost) were assessed for accuracy, precision, recall, F1-score, and AUROC. Feature importance was evaluated using SHAP to interpret the influence of each predictor.
Results
Random Forest achieved the highest performance, with an accuracy of 70 %, precision of 0.69, recall of 0.71, and AUROC of 0.74, closely followed by XGBoost, which achieved an accuracy of 69 % and AUROC of 0.72. SHAP analysis identified the place of delivery, residence, and age group as the most influential predictors of early ANC initiation, with the number of births in the past five years showing a significant negative impact.
Conclusion
Machine learning models, particularly Random Forest and XGBoost, effectively predicted early ANC initiation, highlighting significant demographic and healthcare access-related predictors. These findings suggest targeted interventions focusing on delivery location preferences, residential factors, and age-specific approaches to improve early ANC attendance in Somalia.
{"title":"Machine learning approach in predicting early antenatal care initiation at first trimester among reproductive women in Somalia: an analysis with SHAP explanations","authors":"Jamilu Sani , Mohamed Mustaf Ahmed","doi":"10.1016/j.ibmed.2025.100252","DOIUrl":"10.1016/j.ibmed.2025.100252","url":null,"abstract":"<div><h3>Introduction</h3><div>Timely antenatal care (ANC) initiation is essential for maternal and neonatal health, enabling the early detection of risks and ensuring optimal care. In Somalia, delayed initiation of ANC poses a significant health risk. This study applied machine learning (ML) models to predict early ANC initiation among Somali women and identify key predictors using SHapley Additive exPlanations (SHAP).</div></div><div><h3>Methods</h3><div>Data from the 2020 Somali Health and Demographic Survey were analyzed, focusing on ANC timing in 3138 women aged 15–49. Six ML models (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and XGBoost) were assessed for accuracy, precision, recall, F1-score, and AUROC. Feature importance was evaluated using SHAP to interpret the influence of each predictor.</div></div><div><h3>Results</h3><div>Random Forest achieved the highest performance, with an accuracy of 70 %, precision of 0.69, recall of 0.71, and AUROC of 0.74, closely followed by XGBoost, which achieved an accuracy of 69 % and AUROC of 0.72. SHAP analysis identified the place of delivery, residence, and age group as the most influential predictors of early ANC initiation, with the number of births in the past five years showing a significant negative impact.</div></div><div><h3>Conclusion</h3><div>Machine learning models, particularly Random Forest and XGBoost, effectively predicted early ANC initiation, highlighting significant demographic and healthcare access-related predictors. These findings suggest targeted interventions focusing on delivery location preferences, residential factors, and age-specific approaches to improve early ANC attendance in Somalia.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100252"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877377","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ibmed.2025.100277
Roberto Diaz-Peregrino , Fabian Torres Robles , German Gonzalez , Roberto Palma , Boris Escalante-Ramirez , Jimena Olveres , Juan P. Reyes-Gonzalez , Jose A. Gomez-Coeto , Carlos A. Rodriguez-Herrera
Whole-body magnetic resonance imaging (WB-MRI) is a critical diagnostic tool in clinical practice. However, the manual interpretation of WB-MRI scans is a time-consuming and labor-intensive process. Integrating artificial intelligence (AI) has the potential to streamline these processes, yet the variability in MRI images due to differences in scanner features presents significant challenges for the generalization of AI models across different platforms. This study aims to address these challenges by developing and validating a data augmentation pipeline designed to effectively represent image artifacts from WB-MRI acquisition. The study employs a WB-MRI database to evaluate the generalization power of a segmentation model across platforms, with performance metrics such as the Dice Similarity Coefficient (DSC) and Area Under the Curve (AUC) being reported. The findings suggest that advanced data augmentation techniques can mitigate the impact of scanner variability, thereby enhancing the generalization capabilities of AI models in the context of WB-MRI analysis.
全身磁共振成像（WB-MRI）是临床实践中重要的诊断工具。然而，手动解释WB-MRI扫描是一个耗时和劳动密集型的过程。集成人工智能（AI）有可能简化这些过程，然而，由于扫描仪特征的差异，MRI图像的可变性对人工智能模型在不同平台上的泛化提出了重大挑战。本研究旨在通过开发和验证数据增强管道来解决这些挑战，该管道旨在有效地表示来自WB-MRI采集的图像伪影。该研究采用WB-MRI数据库来评估跨平台分割模型的泛化能力，并报告了Dice Similarity Coefficient （DSC）和Area Under The Curve （AUC）等性能指标。研究结果表明，先进的数据增强技术可以减轻扫描仪可变性的影响，从而增强AI模型在WB-MRI分析背景下的泛化能力。
{"title":"Enhancing generalization in whole-body MRI-based deep learning models: A novel data augmentation pipeline for cross-platform adaptation","authors":"Roberto Diaz-Peregrino , Fabian Torres Robles , German Gonzalez , Roberto Palma , Boris Escalante-Ramirez , Jimena Olveres , Juan P. Reyes-Gonzalez , Jose A. Gomez-Coeto , Carlos A. Rodriguez-Herrera","doi":"10.1016/j.ibmed.2025.100277","DOIUrl":"10.1016/j.ibmed.2025.100277","url":null,"abstract":"<div><div>Whole-body magnetic resonance imaging (WB-MRI) is a critical diagnostic tool in clinical practice. However, the manual interpretation of WB-MRI scans is a time-consuming and labor-intensive process. Integrating artificial intelligence (AI) has the potential to streamline these processes, yet the variability in MRI images due to differences in scanner features presents significant challenges for the generalization of AI models across different platforms. This study aims to address these challenges by developing and validating a data augmentation pipeline designed to effectively represent image artifacts from WB-MRI acquisition. The study employs a WB-MRI database to evaluate the generalization power of a segmentation model across platforms, with performance metrics such as the Dice Similarity Coefficient (DSC) and Area Under the Curve (AUC) being reported. The findings suggest that advanced data augmentation techniques can mitigate the impact of scanner variability, thereby enhancing the generalization capabilities of AI models in the context of WB-MRI analysis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100277"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652996","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ibmed.2025.100282
Timothy Suraj
This paper introduces a novel Bayesian framework integrating Large Language Models (LLMs) into medical history-taking specifically for recurrent medical conditions, aiming to overcome limitations of traditional methods and improve treatment outcomes. Unlike existing AI applications in healthcare that primarily focus on diagnostic classification or prediction in acute settings, our approach emphasizes iterative diagnostic refinement and explainable AI within a Bayesian probabilistic framework, offering a unique strategy for personalized management of recurrent conditions. We empirically evaluate this framework by analyzing the current limitations in clinical history-taking practices and leveraging the capabilities of modern LLMs to generate more comprehensive patient narratives, improve pattern recognition across longitudinal data, and enhance the identification of subtle disease precursors. Our review of preliminary implementations suggests that LLM integration into clinical workflows may reduce diagnostic errors, improve treatment adherence, and enable more personalized therapeutic interventions. However, significant challenges remain regarding clinical validation, privacy concerns, and integration with existing healthcare systems. We conclude that LLMs represent a promising tool for treating recurrent medical conditions when deployed as physician augmentation rather than replacement technologies.
{"title":"A Bayesian framework for LLM-enhanced history-taking in recurrent medical conditions to improve treatment outcomes: An empirical evaluation","authors":"Timothy Suraj","doi":"10.1016/j.ibmed.2025.100282","DOIUrl":"10.1016/j.ibmed.2025.100282","url":null,"abstract":"<div><div>This paper introduces a novel Bayesian framework integrating Large Language Models (LLMs) into medical history-taking specifically for recurrent medical conditions, aiming to overcome limitations of traditional methods and improve treatment outcomes. Unlike existing AI applications in healthcare that primarily focus on diagnostic classification or prediction in acute settings, our approach emphasizes iterative diagnostic refinement and explainable AI within a Bayesian probabilistic framework, offering a unique strategy for personalized management of recurrent conditions. We empirically evaluate this framework by analyzing the current limitations in clinical history-taking practices and leveraging the capabilities of modern LLMs to generate more comprehensive patient narratives, improve pattern recognition across longitudinal data, and enhance the identification of subtle disease precursors. Our review of preliminary implementations suggests that LLM integration into clinical workflows may reduce diagnostic errors, improve treatment adherence, and enable more personalized therapeutic interventions. However, significant challenges remain regarding clinical validation, privacy concerns, and integration with existing healthcare systems. We conclude that LLMs represent a promising tool for treating recurrent medical conditions when deployed as physician augmentation rather than replacement technologies.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100282"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771996","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}
Pub Date : 2025-01-01DOI: 10.1016/j.ibmed.2025.100264
Maria Frasca , Ilaria Cutica , Gabriella Pravettoni , Davide La Torre
Melanoma is one of the most aggressive forms of skin cancer, necessitating advanced diagnostic tools to improve early detection. This study presents a novel AI-driven approach that combines deep neural networks with quantum computing techniques for enhanced lesion classification. Specifically, we employ a U-Net model for segmentation and a hybrid Convolutional Neural Network - Quantum Neural Network (CNN-QNN) for classification. Our approach achieves a precision of 99.67 %, recall of 99.67 %, and an overall accuracy of 99.35 % on the HAM10000 dataset. Additionally, we report a sensitivity of 99.4 %, a specificity of 99.2 %, and a macro F1-score of 99.5 %, significantly surpassing traditional CNN-based classifiers. This hybrid model outperforms conventional deep learning approaches, demonstrating its potential for aiding dermatologists in clinical decision-making. A comparative analysis with state-of-the-art models further validates the effectiveness of our method.
{"title":"Optimizing melanoma diagnosis: A hybrid deep learning and quantum computing approach for enhanced lesion classification","authors":"Maria Frasca , Ilaria Cutica , Gabriella Pravettoni , Davide La Torre","doi":"10.1016/j.ibmed.2025.100264","DOIUrl":"10.1016/j.ibmed.2025.100264","url":null,"abstract":"<div><div>Melanoma is one of the most aggressive forms of skin cancer, necessitating advanced diagnostic tools to improve early detection. This study presents a novel AI-driven approach that combines deep neural networks with quantum computing techniques for enhanced lesion classification. Specifically, we employ a U-Net model for segmentation and a hybrid Convolutional Neural Network - Quantum Neural Network (CNN-QNN) for classification. Our approach achieves a precision of 99.67 %, recall of 99.67 %, and an overall accuracy of 99.35 % on the HAM10000 dataset. Additionally, we report a sensitivity of 99.4 %, a specificity of 99.2 %, and a macro F1-score of 99.5 %, significantly surpassing traditional CNN-based classifiers. This hybrid model outperforms conventional deep learning approaches, demonstrating its potential for aiding dermatologists in clinical decision-making. A comparative analysis with state-of-the-art models further validates the effectiveness of our method.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100264"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501836","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号