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}
Pub Date : 2025-01-01DOI: 10.1016/j.ibmed.2025.100295
Bethel Osuagwu , Hongli Huang , Emily L. McNicol , Vellaisamy A.L. Roy , Aleksandra Vučkovič
Introduction
Motor evoked potentials (MEP) are detected using various methods that determine signal changepoints. The current detection methods perform well given a high signal to noise ratio. However, performance can diminish with artefact such as those arising due to poor signal quality and unwanted electrical potentials. Part of the problem is likely because the methods ignore the morphology of a signal making it impossible to differentiate noise from MEPs.
Methods
For the first time, we investigated a new detection method able to learn MEP morphology using artificial neural networks. To build an MEP detection model, we trained deep neural networks with architectures based on combined CNN and LSTM or self-attention mechanism, using sample MEP data recorded from able-bodied individuals. The MEP detection capability of the models was compared with that of a changepoint based detection method.
Results
Our models reached test accuracy of up to 89.7 ± 1.5 % on average. In a real-world setting evaluation, our models achieved average detection accuracy of up to 94.7 ± 1.2 %, compared with 76.4 ± 5.3 % for the standard changepoint detection method (p = 0.004).
Conclusion
Artificial neural network models can be used for improved automated detection of MEPs.
{"title":"Artificial neural network based automatic detection of motor evoked potentials","authors":"Bethel Osuagwu , Hongli Huang , Emily L. McNicol , Vellaisamy A.L. Roy , Aleksandra Vučkovič","doi":"10.1016/j.ibmed.2025.100295","DOIUrl":"10.1016/j.ibmed.2025.100295","url":null,"abstract":"<div><h3>Introduction</h3><div>Motor evoked potentials (MEP) are detected using various methods that determine signal changepoints. The current detection methods perform well given a high signal to noise ratio. However, performance can diminish with artefact such as those arising due to poor signal quality and unwanted electrical potentials. Part of the problem is likely because the methods ignore the morphology of a signal making it impossible to differentiate noise from MEPs.</div></div><div><h3>Methods</h3><div>For the first time, we investigated a new detection method able to learn MEP morphology using artificial neural networks. To build an MEP detection model, we trained deep neural networks with architectures based on combined CNN and LSTM or self-attention mechanism, using sample MEP data recorded from able-bodied individuals. The MEP detection capability of the models was compared with that of a changepoint based detection method.</div></div><div><h3>Results</h3><div>Our models reached test accuracy of up to 89.7 ± 1.5 % on average. In a real-world setting evaluation, our models achieved average detection accuracy of up to 94.7 ± 1.2 %, compared with 76.4 ± 5.3 % for the standard changepoint detection method (p = 0.004).</div></div><div><h3>Conclusion</h3><div>Artificial neural network models can be used for improved automated detection of MEPs.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100295"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094604","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.2025.100299
Saiprasad Potharaju , Swapnali N. Tambe , Kishore Dasari , N. Srikanth , Rampay Venkatarao , Sagar Tambe
Problem considered
Pneumonia, a global health concern, remains a significant cause of morbidity and mortality, particularly in children under five and the elderly. Diagnostic challenges are pronounced in resource-limited settings, where expertise in radiological interpretation is scarce. X-ray imaging, a common diagnostic tool, often fails to provide accurate results without expert analysis. This gap in timely and precise diagnosis leads to delayed treatments and worsening patient outcomes. The emergence of antibiotic-resistant strains further emphasizes the urgency for innovative diagnostic solutions.
Methods
This research integrates advanced attention mechanisms into convolutional neural networks (CNNs) to enhance pneumonia detection from X-ray images. Utilizing a dataset of 5816 X-rays, preprocessing steps included normalization and data augmentation to improve robustness. The baseline CNN model was augmented with Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) networks, which prioritize critical image regions and recalibrate feature channels. Comparative evaluations were conducted using ResNet50 combined with CBAM.
Results
The CBAM-enhanced CNN achieved 98.6 % accuracy, improving upon the baseline CNN's 92.08 %, with a sensitivity of 98.3 % and specificity of 97.9 %. The SE-integrated CNN followed with 96.25 % accuracy, demonstrating superior feature recalibration. ResNet50 with CBAM attained 93.32 % accuracy. Compared to standard CNN models, these models exhibited reduced overfitting, improved generalization, and enhanced feature extraction. The proposed approach ensures a higher precision rate in detecting pneumonia from X-ray images. The model is designed for real-world clinical applications, particularly in low-resource healthcare settings. A lightweight, user-friendly web application was developed to assist radiologists and general practitioners in automated pneumonia detection, reducing reliance on expert interpretation.
{"title":"Enhanced X-ray image classification for pneumonia detection using deep learning based CBAM and SE mechanisms","authors":"Saiprasad Potharaju , Swapnali N. Tambe , Kishore Dasari , N. Srikanth , Rampay Venkatarao , Sagar Tambe","doi":"10.1016/j.ibmed.2025.100299","DOIUrl":"10.1016/j.ibmed.2025.100299","url":null,"abstract":"<div><h3>Problem considered</h3><div>Pneumonia, a global health concern, remains a significant cause of morbidity and mortality, particularly in children under five and the elderly. Diagnostic challenges are pronounced in resource-limited settings, where expertise in radiological interpretation is scarce. X-ray imaging, a common diagnostic tool, often fails to provide accurate results without expert analysis. This gap in timely and precise diagnosis leads to delayed treatments and worsening patient outcomes. The emergence of antibiotic-resistant strains further emphasizes the urgency for innovative diagnostic solutions.</div></div><div><h3>Methods</h3><div>This research integrates advanced attention mechanisms into convolutional neural networks (CNNs) to enhance pneumonia detection from X-ray images. Utilizing a dataset of 5816 X-rays, preprocessing steps included normalization and data augmentation to improve robustness. The baseline CNN model was augmented with Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) networks, which prioritize critical image regions and recalibrate feature channels. Comparative evaluations were conducted using ResNet50 combined with CBAM.</div></div><div><h3>Results</h3><div>The CBAM-enhanced CNN achieved 98.6 % accuracy, improving upon the baseline CNN's 92.08 %, with a sensitivity of 98.3 % and specificity of 97.9 %. The SE-integrated CNN followed with 96.25 % accuracy, demonstrating superior feature recalibration. ResNet50 with CBAM attained 93.32 % accuracy. Compared to standard CNN models, these models exhibited reduced overfitting, improved generalization, and enhanced feature extraction. The proposed approach ensures a higher precision rate in detecting pneumonia from X-ray images. The model is designed for real-world clinical applications, particularly in low-resource healthcare settings. A lightweight, user-friendly web application was developed to assist radiologists and general practitioners in automated pneumonia detection, reducing reliance on expert interpretation.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100299"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219042","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.2025.100318
Falah Jabar , Lill-Tove Rasmussen Busund , Biagio Ricciuti , Masoud Tafavvoghi , Thomas K. Kilvaer , David J. Pinato , Mette Pøhl , Sigve Andersen , Tom Donnem , David J. Kwiatkowski , Mehrdad Rakaee
Tiling (or patching) histology Whole Slide Images (WSIs) is a required initial step in the development of deep learning (DL) models. Gigapixel-scale WSIs must be divided into smaller, manageable image tiles. Standard WSI tiling techniques often exclude diagnostically important tissue regions or include regions with artifacts such as folds, blurs, and pen-markings, which can significantly degrade DL model performance and analysis. This paper introduces WSI-SmartTiling, a fully automated, deep learning-based, content-aware WSI tiling pipeline designed to include maximal information content from WSI. A supervised DL model for artifact detection was developed using pixel-based semantic segmentation at high magnification (20× and 40x) to classify WSI regions as either artifacts or qualified tissue. The model was trained on a diverse dataset and validated using both internal and external datasets. Quantitative and qualitative evaluations demonstrated its superiority, outperforming state-of-the-art methods with accuracy, precision, recall, and F1 scores exceeding 95 % across all artifact types, along with Dice scores above 94 %. In addition, WSI-SmartTiling integrates a generative adversarial network model to reconstruct tissue regions obscured by pen-markings in various colors, ensuring relevant valuable areas are preserved. Lastly, while excluding artifacts, the pipeline efficiently tiles qualified tissue regions with minimum tissue loss.
In conclusion, this high-resolution preprocessing pipeline can significantly improve pathology WSI-based feature extraction and DL-based classification by minimizing tissue loss and providing high-quality – artifact-free – tissue tiles. The WSI-SmartTiling pipeline is publicly available on GitHub.
{"title":"Fully automatic content-aware tiling pipeline for pathology whole slide images","authors":"Falah Jabar , Lill-Tove Rasmussen Busund , Biagio Ricciuti , Masoud Tafavvoghi , Thomas K. Kilvaer , David J. Pinato , Mette Pøhl , Sigve Andersen , Tom Donnem , David J. Kwiatkowski , Mehrdad Rakaee","doi":"10.1016/j.ibmed.2025.100318","DOIUrl":"10.1016/j.ibmed.2025.100318","url":null,"abstract":"<div><div>Tiling (or patching) histology Whole Slide Images (WSIs) is a required initial step in the development of deep learning (DL) models. Gigapixel-scale WSIs must be divided into smaller, manageable image tiles. Standard WSI tiling techniques often exclude diagnostically important tissue regions or include regions with artifacts such as folds, blurs, and pen-markings, which can significantly degrade DL model performance and analysis. This paper introduces WSI-SmartTiling, a fully automated, deep learning-based, content-aware WSI tiling pipeline designed to include maximal information content from WSI. A supervised DL model for artifact detection was developed using pixel-based semantic segmentation at high magnification (20× and 40x) to classify WSI regions as either artifacts or qualified tissue. The model was trained on a diverse dataset and validated using both internal and external datasets. Quantitative and qualitative evaluations demonstrated its superiority, outperforming state-of-the-art methods with accuracy, precision, recall, and F1 scores exceeding 95 % across all artifact types, along with Dice scores above 94 %. In addition, WSI-SmartTiling integrates a generative adversarial network model to reconstruct tissue regions obscured by pen-markings in various colors, ensuring relevant valuable areas are preserved. Lastly, while excluding artifacts, the pipeline efficiently tiles qualified tissue regions with minimum tissue loss.</div><div>In conclusion, this high-resolution preprocessing pipeline can significantly improve pathology WSI-based feature extraction and DL-based classification by minimizing tissue loss and providing high-quality – artifact-free – tissue tiles. The WSI-SmartTiling pipeline is publicly available on <span><span>GitHub</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683783","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}
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