bims-aukdir 2025-04-20 papers

bims-aukdir

Biomed News

on Automated knowledge discovery in diabetes research

Issue of 2025–04–20
eight papers selected by
Mott Given

Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital.
Detection of diabetic macular oedema patterns with fine-grained image categorisation on optical coherence tomography.
Predict and prevent microvascular complications of type 2 diabetes: a cross-sectional and longitudinal study in Chinese communities.
Development and validation of an insulin resistance prediction model in children and adolescents using machine learning algorithms.
Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.
Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images.
Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.
Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization.

Arch Endocrinol Metab. 2025 Apr 15. 69(2): e230348

Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital.

Carolina Deina, Flavio S Fogliatto, Mateus Augusto Dos Reis, Beatriz D Schaan.

   OBJECTIVE: To characterize, via a predictive model using real-world data, patients with diabetes with a heightened probability of hospitalization.
METHODS: At the Endocrinology Unit of a tertiary public hospital in Rio Grande do Sul, Brazil, a retrospective cohort study analyzed initial consultations from January 1, 2015, to December 31, 2017, focusing on 617 patients with diabetes. Within this group, 82.98% (512 patients) did not require hospitalization, while 17.02% (105 patients) were hospitalized at least once. Multiple machine learning algorithms were tested, and the combination of XGBoost and Instance Hardness Threshold models displayed the best predictive performance. The SHapley Additive exPlanations method was used for result interpretation.
RESULTS: The most optimal performance was observed by combining the XGBoost and Instance Hardness Threshold models, resulting in the highest sensitivity (0.93) in accurately classifying hospitalization events, with an acceptable area under the curve of 0.72. Key predictive features included the number of outpatient visits, amplitude of estimated glomerular filtration rate, and age (individuals below 24 years old and between 65 to 70 years old had higher hospitalization likelihood).
CONCLUSION: The proposed model demonstrated high predictive capability and may help to identify patients with diabetes who should be more closely monitored to reduce their risk of hospitalization.

Keywords:  Diabetes mellitus; Hospitalization; Machine learning

DOI:  https://doi.org/10.20945/2359-4292-2024-0317
BMJ Open Ophthalmol. 2025 Apr 15. pii: e002037. [Epub ahead of print]10(1):

Detection of diabetic macular oedema patterns with fine-grained image categorisation on optical coherence tomography.

Xin Ye, Wangli Qiu, Linzhen Tu, Yingjiao Shen, Qian Chen, Xiangrui Lin, Yaqi Wang, Wenbin Xie, Li-Jun Shen.

   PURPOSE: To develop an artificial intelligence (AI) system for detecting pathological patterns of diabetic macular oedema (DME) with fine-grained image categorisation using optical coherence tomography (OCT) images.
METHODS: The development of the AI system consists of two parts, a pretraining process on a public dataset (Asia Pacific Tele-Ophthalmology Society (APTOS)), and the training process on the local dataset. The local dataset was partitioned into the training set, validation set and test set in the ratio of 6:2:2. The Split Subspace Attention Network (SSA-Net) architecture was adopted to train independent models to detect the seven pathological patterns of DME: intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), hyper-reflective retinal foci (HRF), Müller cell cone disruption (MCCD), subretinal hyper-reflective material (SHRM) and intra-cystic hyper-reflective material (ICHRM). The confusion matrix, sensitivity, specificity and receiver operating characteristic (ROC) were used to evaluate the performance of the models.
RESULTS: The APTOS public dataset consists of 33 853 OCT images and the local dataset consists of 1346 OCT images with DME. In the pretraining process on the public dataset, the accuracy was 0.652 for IRF, 0.928 for SRF, 0.936 for PED and 0.975 for HRF. After the training process on the local dataset, the SSA-Net architecture showed better performance in fine-grained image categorisation on the test set. The area under the ROC curve was 0.980 for IRF, 0.995 for SRF, 0.999 for PED, 0.908 for MCCD, 0.887 for HRF, 0.990 for SHRM and 0.972 for ICHRM. The AI system outputs a heatmap for each result, which can give a visual explanation for the automated identification process. The heatmaps revealed that the regions of interest of the AI system were consistent with the retinal experts.
CONCLUSIONS: The proposed SSA-Net architecture for detecting the pathological patterns of DME achieved satisfactory accuracy. However, this study was conducted in one medical centre, and multicentre trials will be needed in the future.

Keywords:  Imaging; Macula; Pathology

DOI:  https://doi.org/10.1136/bmjophth-2024-002037
Front Endocrinol (Lausanne). 2025 ;16 1541663

Predict and prevent microvascular complications of type 2 diabetes: a cross-sectional and longitudinal study in Chinese communities.

Zhaoxiang Liu, Lianhao Zhou, Wenhui Zhao, Lixia Jin, Jinping Zhang, Yajing Zhang, Yufeng Li, Guixia Deng, Jiquan He, Xinghua Zhao, Wenli Zheng, Yong Tian, Ji Wu, Jianzhong Xiao, Jiandong Gao.

   Purpose: This study investigates the incidence, predictors, and preventive strategies for microvascular complications in type 2 diabetes patients in community settings.
Methods: Data were collected from 3,008 type 2 diabetes patients enrolled across 31 clinics in Beijing and Hebei. Prevalence and incidence of diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic peripheral neuropathy (DPN) were assessed. Predictors were identified using XGBoost and Cox regression, and the impact of lifestyle and multifactorial interventions (MFI) was analyzed.
Results: The prevalence of DKD, DR, and DPN were 39.5%, 26.2%, and 27.1%, respectively, with incidences of 74, 21, and 28 per 1000-person year. XGBoost identified that diabetes duration, age, HbA1c, FBG, triglyceride, BP, serum creatinine, proteinuria, aspirin and statin use were associated with those microvascular complications. The risk of DKD increased more rapidly as HbA1c exceeded 7.5% and decreased as blood pressure was maintained below 120/70 mmHg. Cox regression models showed that community-based intervention, including lifestyle modifications, were associated with a lower risk of DR and DPN. The study also found that higher variability in HbA1c and albumin-to-creatinine ratio (ACR) was associated with an increased risk of microvascular complications.
Conclusions: Community-based interventions significantly reduce the of DR and DPN, highlighting the need for individualized glycemic and BP management in primary care. The findings emphasize the importance of comprehensive management strategies to prevent the development and progression of microvascular complications in type 2 diabetes patients.
Clinical trial registration: http://www.chictr.org.cn/, identifier ChiCTR-TRC-13003222.

Keywords:  diabetes kidney disease; diabetic peripheral neuropathy; diabetic retinopathy; machine learning; type 2 diabetes

DOI:  https://doi.org/10.3389/fendo.2025.1541663
Transl Pediatr. 2025 Mar 31. 14(3): 452-462

Development and validation of an insulin resistance prediction model in children and adolescents using machine learning algorithms.

Xiu Huang, Kun Yi, Lin Jia, Yinmei Li, Hui He, Can Ma, Xiao Fang.

   Background: Insulin resistance (IR) is a precursor to metabolic disorders like type 2 diabetes and hypertension in children and adolescents. Early detection of IR is critical to prevent severe metabolic complications. IR is influenced by factors such as diet, inflammation, and genetics. However, existing studies often focus on limited populations and overlook dietary factors. This study aimed to evaluate the use of machine learning (ML) models for early IR prediction in children and adolescents, emphasizing accuracy.
Methods: We used physical examination data of children and adolescents aged 6-17 years from the China Health and Nutrition Survey (CHNS) database as the training set and collected routine physical examination data from children and adolescents aged 6-17 years admitted to Nanchong Central Hospital and the Nanchong City Jialing District People's Hospital in Sichuan Province from January 2019 to October 2024 for validation. IR was assessed using the Homeostatic Model Assessment for IR (HOMA-IR) score, with a cutoff of >3.0 indicating IR. Potential predictors included demographic details, lifestyle habits, and blood test results. We conducted univariate logistic regression (LR) analysis to select variables with statistical significance and then constructed and compared the back propagation neural network (BPNN), exhaustive Chi-squared automatic interaction detector (E-CHAID), support vector machine (SVM), and LR models.
Results: The training sample included 827 children and adolescents (281 with IR and 546 without IR), while the test sample included 207 participants. The SVM model demonstrated superior predictive accuracy (91.90% in training and 90.34% in test set) compared to the E-CHAID (77.75% in training and 72.95% in test set), BPNN (75.94% in training and 70.05% in test set), and LR models (76.18% in training and 71.01% in test set). Sensitivity, specificity, Youden's index, and area under the curve (AUC) values also favored the SVM model in both training and test samples.
Conclusions: Compared with the E-CHAID, BPNN, and LR models, the SVM model exhibited superior predictive ability for IR in children and adolescents based on physical examination data that include dietary factors. These findings suggest that the SVM model could serve as a valuable tool for early clinical prediction of IR, potentially aiding in the prevention of type 2 diabetes mellitus (T2DM) and associated metabolic complications. Further research is needed to validate these results in larger and more diverse populations.

Keywords:  Insulin resistance (IR); adolescent; child; machine learning (ML); physical examination

DOI:  https://doi.org/10.21037/tp-2024-502
Sci Rep. 2025 Apr 18. 15(1): 13389

Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.

Xuemei Liang, Shaozhao Luo, Zhigao Liu, Yunsheng Liu, Shinan Luo, Kaiqing Zhang, Li Li.

  This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEGF) treatment outcomes using an unsupervised machine learning (ML) approach based on radiomic features extracted from pre-treatment optical coherence tomography (OCT) images. Retrospective data from 234 eyes with DME treated with three anti-VEGF therapies between January 2020 and March 2024 were collected from two clinical centers. Radiomic analysis was conducted on pre-treatment OCT images. Following principal component analysis (PCA) for dimensionality reduction, two unsupervised clustering methods (K-means and hierarchical clustering) were applied. Baseline characteristics and treatment outcomes were compared across clusters to assess clustering efficacy. Feature selection employed a three-stage pipeline: exclusion of collinear features (Pearson's r > 0.8); sequential filtering through ANOVA (P < 0.05) and Boruta algorithm (500 iterations); multivariate stepwise regression (entry criteria: univariate P < 0.1) to identify outcome-associated predictors. From 1165 extracted radiomic features, four distinct DME clusters were identified. Cluster 4 exhibited a significantly lower incidence of residual/recurrent DME (RDME) (34.29%) compared to Clusters 1-3 (P = 0.003, P = 0.005 and P = 0.002, respectively). This cluster also demonstrated the highest proportion of eyes (71.43%) with best-corrected visual acuity (BCVA) exceeding 20/63 (P = 0.003, P = 0.005 and P = 0.002, respectively). Multivariate analysis identified logarithm_gldm_DependenceVariance as an independent risk factor for RDME (OR 1.75, 95% CI 1.28-2.40; P < 0.001), while Wavelet-LH_Firstorder_Mean correlated with worse visual outcomes (OR 8.76, 95% CI 1.22-62.84; P = 0.031). Unsupervised ML leveraging pre-treatment OCT radiomics successfully stratifies DME eyes into clinically distinct subgroups with divergent therapeutic responses. These quantitative features may serve as non-invasive biomarkers for personalized outcome prediction and retinal pathology assessment.

Keywords:  Anti-vascular endothelial growth factor; Diabetic macular edema; Machine learning; OCT-omics feature; Treatment outcomes

DOI:  https://doi.org/10.1038/s41598-025-96988-3
Ophthalmol Sci. 2025 Jul-Aug;5(4):5(4): 100722

Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images.

Idan Bressler, Rachelle Aviv, Danny Margalit, Gal Yaakov Cohen, Tsontcho Ianchulev, Shravan V Savant, David J Ramsey, Zack Dvey-Aharon.

   Objective: To develop and validate a deep learning model for diabetic macular edema (DME) detection using color fundus imaging, which is applicable in a diverse, multidevice clinical setting.
Design: Evaluation of diagnostic test or technology.
Subjects: A deep learning model was trained for DME detection using the EyePACS dataset, consisting of 32 049 images from 15 892 patients. The average age was 55.02%, and 51% of the patients were women.
Methods: Data were randomly assigned, by participant, into development (n = 14 246) and validation (n = 1583) sets. Analysis was conducted on the single image, eye, and patient levels. Model performance was evaluated using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Independent validation was further performed on the Indian Diabetic Retinopathy Image Dataset, as well as on new data.
Main Outcome Measures: Sensitivity, specificity, and AUC.
Results: At the image level, a sensitivity of 0.889 (95% confidence interval [CI]: 0.878, 0.900), a specificity of 0.889 (95% CI: 0.877, 0.900), and an AUC of 0.954 (95% CI: 0.949, 0.959) were achieved. At the eye level, a sensitivity of 0.905 (95% CI: 0.890, 0.920), a specificity of 0.902 (95% CI: 0.890, 0.913), and an AUC of 0.964 (95% CI: 0.958, 0.969) were achieved. At the patient level, a sensitivity of 0.900 (95% CI: 0.879, 0.917), a specificity of 0.900 (95% CI: 0.883, 0.911), and an AUC of 0.962 (95% CI: 0.955, 0.968) were achieved.
Conclusions: Diabetic macular edema can be detected from color fundus imaging with high performance on all analysis metrics. Automatic DME detection may simplify screening, leading to more encompassing screening for diabetic patients. Further prospective studies are necessary.
Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Keywords:  Artificial intelligence; Deep learning; Diabetic macular edema; Fundus

DOI:  https://doi.org/10.1016/j.xops.2025.100722
J Diabetes Metab Disord. 2025 Jun;24(1): 104

Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.

Navid Sobhi, Yasin Sadeghi-Bazargani, Majid Mirzaei, Mirsaeed Abdollahi, Ali Jafarizadeh, Siamak Pedrammehr, Roohallah Alizadehsani, Ru-San Tan, Sheikh Mohammed Shariful Islam, U Rajendra Acharya.

   Background: Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vasculature imaging serves as a valuable indicator of both microvascular and macrovascular health. Moreover, artificial intelligence (AI)-enabled systems developed for high-throughput detection of diabetic retinopathy (DR) using digitized retinal images have become clinically adopted. This study reviews AI applications using retinal images for DM-related complications, highlighting advancements beyond DR screening, diagnosis, and prognosis, and addresses implementation challenges, such as ethics, data privacy, equitable access, and explainability.
Methods: We conducted a thorough literature search across several databases, including PubMed, Scopus, and Web of Science, focusing on studies involving diabetes, the retina, and artificial intelligence. We reviewed the original research based on their methodology, AI algorithms, data processing techniques, and validation procedures to ensure a detailed analysis of AI applications in diabetic retinal imaging.
Results: Retinal images can be used to diagnose DM complications including DR, neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as to predict the risk of cardiovascular events. Beyond DR screening, AI integration also offers significant potential to address the challenges in the comprehensive care of patients with DM.
Conclusion: With the ability to evaluate the patient's health status in relation to DM complications as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.

Keywords:  Artificial intelligence; Diabetes complications; Diabetes mellitus; Diabetic retinopathy; Retina

DOI:  https://doi.org/10.1007/s40200-025-01596-7
MethodsX. 2025 Jun;14 103232

Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization.

Indresh Kumar Gupta, Shruti Patil, Supriya Mahadevkar, Ketan Kotecha, Awanish Kumar Mishra, Joel J P C Rodrigues.

  Diabetic retinopathy (DR) is a serious complication of diabetes that can result in vision loss if untreated, often progressing silently without warning symptoms. Elevated blood glucose levels damage the retina's microvasculature, initiating the condition. Early detection through retinal fundus imaging, supported by timely analysis and treatment, is critical for managing DR effectively. However, manually inspecting these images is a labour-intensive and time-consuming process, making computer-aided diagnosis (CAD) systems invaluable in supporting ophthalmologists. This research introduces the Fundus Imaging Diabetic Retinopathy Classification using Deep Learning and Fennec Fox Optimization (FIDRC-DLFFO) model, which automates the identification and classification of DR. The model integrates several advanced techniques to enhance performance and accuracy.1.The proposed FIDRC-DLFFO model automates DR detection and classification by combining median filtering for noise reduction, Inception-ResNet-v2 for feature extraction, and a gated recurrent unit (GRU) for classification.2.Fennec Fox Optimization (FFO) fine-tunes the GRU hyperparameters, boosting classification accuracy, with its effectiveness demonstrated on benchmark datasets.3.The results provide insights into the model's effectiveness and potential for real-world application.

Keywords:  Deep learning; Diabetic retinopathy; Fennec fox optimization; Fundus Imaging Diabetic Retinopathy Classification using Deep Learning and Fennec Fox Optimization (FIDRC-DLFFO) model; Inception-ResNet-v2; Median filter

DOI:  https://doi.org/10.1016/j.mex.2025.103232