Informatics in Medicine Unlocked最新文献

{"title":"TransUNetB: An advanced Transformer–UNet framework for efficient and explainable brain tumor segmentation","authors":"Katura Gania Khushubu ,&nbsp;Abdullah Al Masum ,&nbsp;Md Habibur Rahman ,&nbsp;Shakh Md Shakib Hasan ,&nbsp;Md Imranul Hoque Bhuiyan ,&nbsp;Mohammad Rasel Mahmud ,&nbsp;S.M. Masfequier Rahman Swapno ,&nbsp;Abhishek Appaji","doi":"10.1016/j.imu.2025.101706","DOIUrl":"10.1016/j.imu.2025.101706","url":null,"abstract":"<div><div>This study presents TransUNetB, a hybrid architecture that combines Transformer and UNet for multi-class brain tumor segmentation. This model integrates global context modeling with precise spatial localization. A lightweight Transformer encoder at the bottleneck captures long-range dependencies, while the U-Net's skip pathways preserve fine anatomical details. Additionally, a multi-scale decoder fusion module consolidates features at various resolutions, enhancing the clarity of tumor boundaries in heterogeneous, low-contrast conditions. Our contributions are threefold: (1) a simple, efficient design that integrates bottleneck self-attention with multi-scale fusion for robust ED/TC/ET segmentation; (2) a comprehensive ablation of design choices—attention type, positional encoding, fusion strategy, loss formulation, and patch size—quantifying their impact on accuracy and efficiency; and (3) an explainability analysis using Grad-CAM with quantitative focus/entropy measures to verify that salient regions align with clinical tumor substructures. Evaluated on the BraTS 2020 and BraTS 2021 datasets, TransUNetB achieves a Dice score of 98.90 % and an Intersection over Union (IoU) score of 96.10 %. It outperforms strong CNN and vision-transformer baselines while maintaining a competitive runtime of approximately 63 ms per image. These results suggest that combining global attention with spatially faithful decoding provides a favorable trade-off between accuracy and efficiency for clinical deployment. We also discuss the generalization of our model beyond MRI cohorts, practical constraints in resource-limited settings, and future research avenues, including attention-guided fusion and broader multi-center validation.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"59 ","pages":"Article 101706"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145463705","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":"Use of natural language processing to predict the diagnoses of patients with gait impairments","authors":"Bastian Widmer ,&nbsp;Giusi Moffa ,&nbsp;H. Elke Viehweger ,&nbsp;Morgan Sangeux","doi":"10.1016/j.imu.2025.101712","DOIUrl":"10.1016/j.imu.2025.101712","url":null,"abstract":"<div><h3>Background</h3><div>In clinical practice, unstructured medical data stored in electronic health records presents challenges for information extraction. This study aims to evaluate the feasibility of using natural language processing (NLP) techniques on German clinical text, as well as its English version, translated with Google Translate, for automating the diagnosis classification of patients with gait impairments.</div></div><div><h3>Materials and methods</h3><div>We trained four different types of models on a dataset containing 892 diagnoses to classify patients into four subgroups: unilateral cerebral palsy (CP), bilateral CP, undefined CP, and no CP. The approaches included rule-based methods, term frequency, embeddings, and Bidirectional Encoder Representations from Transformers. We assessed model performance using metrics such as accuracy, precision, recall, and F₁-Measure. To compare the classification results between the original German text and the translated English text, we applied McNemar's test.</div></div><div><h3>Results</h3><div>All models exhibited strong and comparable performance across the evaluated metrics, except for embedding-based models. There was no statistically significant difference in the misclassification distributions when the models were applied to the German text compared to its English translation.</div></div><div><h3>Conclusions</h3><div>These findings underscore the potential of NLP to transform medical informatics by accurately and efficiently classifying orthopaedic texts in German. Furthermore, translating diagnoses from German into English is a viable approach, facilitating the use of models trained on English text for multilingual diagnosis classification.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"59 ","pages":"Article 101712"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145526150","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":"Exploring health informaticians' perception of the Saudi health informatics competency framework (SHICF)","authors":"Khulud Alkadi ,&nbsp;Lujain Aljarallah ,&nbsp;Hadeel Albedewi","doi":"10.1016/j.imu.2025.101709","DOIUrl":"10.1016/j.imu.2025.101709","url":null,"abstract":"<div><h3>Background</h3><div>Saudi Arabia has recently made significant progress in health informatics, investing in eHealth infrastructure and training health informaticians. The Saudi Health Informatics Competency Framework (SHICF), established in 2021, ensures health informaticians possess the skills and knowledge to address twenty-first-century healthcare challenges and support Saudi Vision 2030.</div></div><div><h3>Objectives</h3><div>This study explores practicing health informaticians' perceptions of the current SHICF in Saudi Arabia, aiming to suggest improvements to the framework.</div></div><div><h3>Methods</h3><div>This qualitative descriptive cross-sectional study used semi-structured interviews, with health informaticians in Riyadh, Saudi Arabia, from academia, public, and private sectors. Interviews included demographics and 20 questions exploring perceptions of SHICF. Inductive thematic analysis identified themes and sub-themes reflecting participants’ views on SHICF.</div></div><div><h3>Results</h3><div>A total of 10 interviewees were included in the study. Most viewed the current SHICF as comprehensive but requiring clarification. They also suggested tailoring competencies to Saudi Arabian laws, technical standards, and various educational levels.</div></div><div><h3>Conclusion</h3><div>Our findings highlight the need to refine the Saudi Health Informatics Competency Framework (SHICF) to a more tailored, current version that reflects recent developments in Saudi health informatics.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"59 ","pages":"Article 101709"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145526151","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":"Domain specific transfer learning and classifier chains in Alzheimer's disease detection using 3D convolutional neural networks","authors":"Lisa-Marie Bente ,&nbsp;Luca Himstedt ,&nbsp;Tim Kacprowski ,&nbsp;Alzheimer's Disease Neuroimaging Initiative","doi":"10.1016/j.imu.2025.101711","DOIUrl":"10.1016/j.imu.2025.101711","url":null,"abstract":"<div><div>This study examines different configurations of deep convolutional neural networks (CNNs) and the effect of using domain-specific transfer learning for distinguishing Alzheimer's Disease and Mild Cognitive Impairment from normal controls. The data used to train our models was provided by ADNI and included 1118 3D FDG-PET scans in total. We train a binary and a multiclass classifier, as well as chains of binary classifiers, for consecutive multiclass classification. Two chains were trained with different orders: chain A classified cognitively normal (CN) vs. non-CN, followed by Alzheimer's disease (AD) vs. mild cognitive impairment (MCI). Classifier chain B classified AD vs. non-AD first, followed by MCI vs. CN. All classifiers were trained with and without the use of domain-specific transfer learning, using weights from Med3D. All models achieve comparable performance to the state-of-the-art. Classifier chain A even achieved superior performance with an accuracy of 96 %, F1 score of 95 % and AUROC of 99 %. Using domain-specific transfer learning resulted in worse performance among the majority of the models, producing decreases in accuracy of up to 55 %. These results show the potential of binary classifier chains and open some questions about the use of domain-specific transfer learning.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"59 ","pages":"Article 101711"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145526152","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":"Dynamic system of tuberculosis elimination modelling in Indonesia: A study in Kuningan, West Java, Indonesia","authors":"Dyah Aryani Perwitasari ,&nbsp;Imaniar Noor Faridah ,&nbsp;Casnan Casnan ,&nbsp;Nourma Nurjanah ,&nbsp;Ina Listiana ,&nbsp;Hendy Ristiono ,&nbsp;Juwita Ramadhani","doi":"10.1016/j.imu.2025.101714","DOIUrl":"10.1016/j.imu.2025.101714","url":null,"abstract":"<div><div>Tuberculosis (TB) remains a major public health burden in Indonesia, which currently ranks second worldwide in TB incidence. This study aimed to develop and validate a dynamic systems model to simulate TB elimination strategies at the district level in Kuningan, West Java. We conducted a cross-sectional study involving 30 TB patients, complemented with retrospective data from Ciniru Health Center, the national TB surveillance system (SITB), and Minimal Standard of Services (2019–2023). Patient-level data were collected using structured questionnaires on adherence, beliefs, and knowledge, as well as household environmental assessments. A system dynamics model was constructed using Powersim Studio 10 Academic to simulate the transmission and control of TB. Model validation against historical data showed strong predictive accuracy (absolute mean error &lt;5 %). Simulation of two scenarios—Moderate and extreme—revealed substantial differences in outcomes by 2040. Under the Moderate scenario, improvements in lifestyle, nutrition, and adherence reduced the number of TB cases from 82 to 55, and suspects treated decreased from 702 to 614. Under the extreme scenario, further reductions were observed, with TB cases declining to 10 and suspects treated to 612. Key determinants influencing TB burden included nutrition, lifestyle, household environment, and management of drug side effects. This dynamic modeling framework provides valuable insights for localized TB control strategies. It emphasizes that elimination efforts should go beyond biomedical interventions by addressing behavioral, environmental, and psychosocial determinants. The model offers a practical tool to guide district-level planning and supports the national TB elimination agenda.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"59 ","pages":"Article 101714"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stochastic model for evaluating the progression of ductal carcinoma in situ breast cancer using Norwegian breast cancer screening program data","authors":"Xiaoxue Li ,&nbsp;Harald Weedon-Fekjær ,&nbsp;Bo Zhang ,&nbsp;Sandra J. Lee","doi":"10.1016/j.imu.2025.101647","DOIUrl":"10.1016/j.imu.2025.101647","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Following widespread mammography screening for breast cancer, the incidence of ductal carcinoma in situ (DCIS) has increased sharply. However, the value of detecting DCIS by screening is uncertain as not all DCIS progresses to invasive breast cancer. Knowledge about the sojourn time in the screen-detectable DCIS state and the progression or regression of DCIS to other stages (i.e., the natural history of DCIS) is essential to treat screen-detected DCIS lesions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We developed a stochastic model for DCIS natural history, characterized by DCIS states, invasive breast cancer states, and transition probabilities between the states. The model included DCIS lesions in the screen-detectable preclinical state and their progression to clinical DCIS, invasive breast cancer, or regression to a state undetectable by screening. Unlike currently available DCIS Markov models, the proposed model assumed no relationship between the sojourn time and transition probabilities in DCIS states and used age-specific transition probabilities. In the absence of ideal data for DCIS modeling, the Norwegian Breast Cancer Screening Program data, specifically arranged by screening round and mode of detection, was applied to obtain maximum likelihood estimates of DCIS natural history parameters, including transition probabilities and the mean sojourn time in the preclinical screen-detectable DCIS state.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;By indirectly specifying a range of the proportion of breast lesions in the preclinical undetectable DCIS state (S&lt;sub&gt;du&lt;/sub&gt;) that progress through the preclinical screen-detectable DCIS state (S&lt;sub&gt;dp&lt;/sub&gt;), &lt;em&gt;P&lt;/em&gt;&lt;sub&gt;&lt;em&gt;d&lt;/em&gt;&lt;/sub&gt;&lt;em&gt;(t)&lt;/em&gt;, not going directly to preclinical invasive breast cancer (S&lt;sub&gt;p&lt;/sub&gt;), plausible sets of DCIS natural history parameters were systematically evaluated. All estimates indicated that the mean sojourn time in S&lt;sub&gt;dp&lt;/sub&gt; was relatively short (≤3.5 years). For the age group 50–54 years, the best fitting mean sojourn time in S&lt;sub&gt;dp&lt;/sub&gt; was 3.4–3.5 years, with mammography sensitivity 0.60–0.61 when &lt;em&gt;P&lt;/em&gt;&lt;sub&gt;&lt;em&gt;d&lt;/em&gt;&lt;/sub&gt;&lt;em&gt;(t)&lt;/em&gt; was 0.31–0.34. When &lt;em&gt;P&lt;/em&gt;&lt;sub&gt;&lt;em&gt;d&lt;/em&gt;&lt;/sub&gt;&lt;em&gt;(t)&lt;/em&gt; was larger, mean sojourn times in S&lt;sub&gt;dp&lt;/sub&gt; likely varied by the pathway. In general, assuming higher &lt;em&gt;P&lt;/em&gt;&lt;sub&gt;&lt;em&gt;d&lt;/em&gt;&lt;/sub&gt;&lt;em&gt;(t)&lt;/em&gt;—that is, a higher proportion of DCIS lesions that progress to from S&lt;sub&gt;dp&lt;/sub&gt; to S&lt;sub&gt;p&lt;/sub&gt;—the mean sojourn time became shorter. Regression to no cancer or undetectable state might be possible, but the quantified level of regression was associated with great uncertainties.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;While difficult to point to a unique set of DCIS natural history estimates, identifying broader sets of plausible estimates is possible. Estimates reported here provide a comprehensive view of potential progression paths of DCIS while acknowledging the limit","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101647"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899764","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":"Multi-model deep learning approach for the classification of kidney diseases using medical images","authors":"Waleed Obaid ,&nbsp;Abir Hussain ,&nbsp;Tamer Rabie ,&nbsp;Dhafar Hamed Abd ,&nbsp;Wathiq Mansoor","doi":"10.1016/j.imu.2025.101663","DOIUrl":"10.1016/j.imu.2025.101663","url":null,"abstract":"<div><div>Renal impairment poses a risk across all ages. With the global nephrologist shortage, the rising public health concerns over kidney failure, and advancements in technology, there is a growing need for an AI system capable of identifying kidney anomalies automatically. Chronic kidney disease is marked by a gradual failure in kidney function due to various factors, such as stones, cysts, and tumors. Chronic kidney disease often presents without noticeable symptoms initially, leading to cases remaining untreated until advanced stages. Tumors, which are dense tissue masses, can directly harm organs, including glands and spinal cells. Kidney stone disease, or urolithiasis, occurs when many solids accumulate in the urinary tract, leading to stone formation. This research paper leveraged a deep learning approach to address the worldwide shortage of urologists by facilitating the detection of kidney diseases. A novel deep learning technique is proposed using Darknet53 for the classification of kidney diseases using a large dataset gathered from five resources. The total number of images is 27,145 scans of the entire abdomen and urogram, focusing on common kidney conditions, including stones, cysts, and tumors. The data was grouped into four classes: normal, cyst, tumor, and stone. The proposed technique involves the use of 16 deep-learning models to obtain enhanced performance based on accuracy, recall, specificity, and precision, offering new potential for detecting kidney abnormalities. Model performance was evaluated, achieving 99.69 %, 0.31 %, 99.66 %, 99.88 %, 99.77 %, 0.12 %, 99.71 %, 99.60 %, and 99.17 % for accuracy, error, recall, specificity, precision, false positive rate, F1_score, Matthews Correlation Coefficient, and Kappa, respectively. Our simulation results using the Fuzzy Decision by Opinion Score Method indicated that the Darknet53 generated the best results for detecting kidney abnormalities.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101663"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298316","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":"In-context learning for label-efficient cancer image classification in oncology","authors":"Mobina Shrestha ,&nbsp;Bishwas Mandal ,&nbsp;Vishal Mandal ,&nbsp;Asis Shrestha ,&nbsp;Amir Babu Shrestha","doi":"10.1016/j.imu.2025.101683","DOIUrl":"10.1016/j.imu.2025.101683","url":null,"abstract":"<div><div>The application of artificial intelligence in oncology has been limited by its reliance on large, annotated datasets and the need for retraining models for domain-specific diagnostic tasks. Taking heed of these limitations, we investigated in-context learning as a pragmatic alternative to model retraining by allowing models to adapt to new diagnostic tasks using only a few labeled examples at inference, without the need for retraining. Using four vision-language models (VLMs) -- Paligemma, CLIP, ALIGN and GPT-4o, we evaluated the performance across three oncology datasets: MHIST, PatchCamelyon and HAM10000. To the best of our knowledge, this is the first study to compare the performance of multiple VLMs with in-context learning on different oncology classification tasks. Without any parameter updates, all models showed significant gains with few-shot prompting, with GPT-4o reaching an F1 score of 0.81 in binary classification and 0.60 in multi-class classification settings. While these results remain below the ceiling of fully fine-tuned systems, they highlight the potential of ICL to approximate task-specific behavior using only a handful of examples, reflecting how clinicians often reason from prior cases. Notably, open-source models like Paligemma and CLIP demonstrated competitive gains despite their smaller size, suggesting feasibility for deployment in computing constrained clinical environments. Overall, these findings highlight the potential of ICL as a practical solution in oncology, particularly for rare cancers and resource-limited contexts where fine-tuning is infeasible and annotated data is difficult to obtain.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"58 ","pages":"Article 101683"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887256","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":"Early detection and classification of diabetic retinopathy by transfer learning of NASNet-large and ResNet-50 convolutional neural networks","authors":"Sreebhadra Vallukappully ,&nbsp;Ian van der Linde ,&nbsp;Ashim Chakraborty","doi":"10.1016/j.imu.2025.101688","DOIUrl":"10.1016/j.imu.2025.101688","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR) is a progressive eye disease that affects those with long-term diabetes. It can lead to irreversible blindness if not detected and treated early. Early detection is challenging as changes to the retina are initially subtle. A number of computational models have been proposed to detect DR in fundus images, including in its early stages. Here, a novel transfer learning approach is proposed using the NASNet-Large and ResNet-50 convolutional neural networks. Image pre-processing steps are tested combinatorically. Class imbalance is addressed with oversampling and data augmentation to give trustworthy performance metrics. The models give impressive detection rates using a standard dataset containing expert-labelled DR fundus images (APTOS 2019), with the best performing models giving accuracy in classifying unseen images exceeding 0.96 (F1 score 0.97) for Early-stage DR detection (no DR vs mild and moderate), and over 0.91 accuracy (F1 score 0.91) for Multi-stage classification (no DR, mild, moderate, severe, and proliferative). This work highlights the potential of combining the transfer learning of state-of-the-art deep learning models with classical image processing for effective DR detection and classification.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"58 ","pages":"Article 101688"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010545","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":"Examining the association between genetic polymorphisms and osteoporosis among post-menopausal women: a systematic review","authors":"Zainab Alhalwachi ,&nbsp;Mira Mousa ,&nbsp;Salsabeel Juneidi ,&nbsp;Gabriela Restrepo-Rodas ,&nbsp;Spyridon Karras ,&nbsp;Habiba Alsafar ,&nbsp;Fatme Al Anouti","doi":"10.1016/j.imu.2025.101652","DOIUrl":"10.1016/j.imu.2025.101652","url":null,"abstract":"<div><h3>Purpose</h3><div>Postmenopausal osteoporosis (PMOP) is the most prevalent metabolic bone disease among women, characterized by significant bone density loss and increased fracture risk. With a genetic component, a systematic review was conducted on the association between genetic polymorphisms and PMOP risk.</div></div><div><h3>Methods</h3><div>A comprehensive review of PubMed literature examined genetic polymorphisms linked to PMOP risk. The primary outcome was to identify the most frequently studied genes linked to PMOP. The secondary outcome was to perform a meta-analysis on the top genetic markers to assess their overall association with PMOP risk.</div></div><div><h3>Results</h3><div>Six genes, accounting for 55.08 % of all studies, were strongly associated with PMOP. Of these, the <em>VDR</em> gene was featured in 35 articles (18.72 % of studies), TNFRSF11B in 23 (12.30 %), <em>ESR1</em> in 18 (9.63 %), <em>COL1A1</em> in 12 (6.42 %), <em>MTHFR</em> in 8 (4.27 %), and TGFb1 in 7 (3.74 %). Meta-analysis showed five markers significantly associated with PMOP: SNP rs1544410 (OR_G: 0.74 (0.59, 0.92)), SNP rs11568820 (OR_G: 1.40 (1.03, 1.91)), and SNP rs2228570 (OR_T: 1.39 (1.12, 1.73)) in the <em>VDR</em> gene; and PvuII variant (OR_P: 0.80 (0.67, 0.96)) in the <em>ESR1</em> gene.</div></div><div><h3>Conclusion</h3><div>This review strengthens the importance of conducting a robust, multi-ethnic, large cohort study with functional analysis to corroborate the findings of the six key genes associated with PMOP. Replicating these findings in larger and more diverse datasets is crucial to validate their biological relevance and potential clinical application.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101652"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070359","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}