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Medical imaging (X-ray, CT, MRI), electronic health records, clinical trials, ECG/EEG, pathology
12,687 datasets
A systematic review and meta-analysis by Fei Liu, published on figshare in 2026, comparing surgical outcomes for choledochal cyst in children. The analysis includes 19 retrospective studies sourced from PubMed, Embase, Web of Science, and the Cochrane Library up to December 31, 2025. It evaluates safety and efficacy metrics such as operative time, blood loss, and postoperative complications.
A meta-analysis of 19 retrospective studies comparing robot-assisted and laparoscopic surgery for choledochal cyst in children. The dataset includes statistical results for outcomes like operative time, blood loss, and postoperative complications, derived from a systematic search of PubMed, Embase, Web of Science, and the Cochrane Library up to December 31, 2025. The analysis was performed by Fei Liu and published on figshare in 2026.
188 lateral cephalogram images from children at two hospitals between 2021 and 2025 were used to develop an AI diagnostic framework. The proposed two-stage model segments the upper airway and classifies obstructive sleep apnea, achieving a mean Dice Similarity Coefficient of 0.931 for segmentation and an AUC of 0.945 for classification. Author Jiayi Zhang released this research dataset on figshare under a CC-BY-4.0 license.
A retrospective cohort study of 139 hospitalized patients with diabetic foot ulcers from a regional referral center in Northwest China between January 2020 and January 2025. The dataset was used to evaluate the prognostic value of the C-reactive protein-to-albumin ratio for predicting 6-month major adverse limb events. The study was authored by Aihemaitijiang Aihetaier and shared under a CC-BY-4.0 license.
139 patient records from a retrospective cohort study at a regional referral center in Northwest China, collected between January 2020 and January 2025. The data was used to evaluate the C-reactive protein-to-albumin ratio (CAR) as a predictor for 6-month major adverse limb events (MALE). Author Aihemaitijiang Aihetaier published the dataset on figshare in April 2026 under a CC-BY-4.0 license.
A retrospective cohort study of 139 hospitalized patients with diabetic foot ulcers from a regional referral center in Northwest China between January 2020 and January 2025. The dataset, authored by Aihemaitijiang Aihetaier and shared on figshare, evaluates the prognostic value of the C-reactive protein-to-albumin ratio for predicting 6-month major adverse limb events.
From 2018 through 2023, a retrospective cohort study enrolled 8,678 participants who underwent health screenings at Kuichong People’s Hospital in Shenzhen. The research investigates the association between the TyG-GGT index, a novel insulin resistance marker, and incident diabetes mellitus risk. The dataset, authored by Yajing Gao and last updated in 2026, includes results from Cox regression and ROC curve analyses.
48 studies on deep learning models for aortic dissection segmentation or diagnosis were included in this systematic review and meta-analysis. The analysis reports performance metrics, including a mean Dice coefficient of 89.2% for false lumen segmentation and pooled sensitivity of 0.94 for CT-based diagnostic models. The document was authored by Yichen Zhao and last updated in April 2026.
48 studies on deep learning models for aortic dissection segmentation or diagnostic tasks were included in this systematic review and meta-analysis. The document, authored by Yichen Zhao and last updated in April 2026, reports performance metrics such as a mean Dice coefficient of 89.2% for false lumen segmentation and pooled sensitivity of 0.94 for CT-based diagnosis.
48 studies on deep learning models for aortic dissection segmentation or diagnosis were included in this systematic review and meta-analysis. The analysis reports performance metrics, including a mean Dice coefficient of 89.2% for false lumen segmentation and pooled sensitivity of 0.94 for CT-based diagnostic models. The dataset, authored by Yichen Zhao and last updated in April 2026, aggregates findings from studies identified up to November 2024.
A systematic review and meta-analysis aggregating results from 48 studies on deep learning models for aortic dissection segmentation and diagnosis, published up to November 3, 2024. The data, compiled by author Yichen Zhao, includes performance metrics such as Dice coefficients and pooled sensitivity/specificity for models based on CT, CTA, and ECG imaging. The dataset is a 22.1 KB document summarizing the findings of the review.
48 studies on deep learning models for aortic dissection segmentation or diagnostic tasks were included in this systematic review. The supplementary file, authored by Yichen Zhao and last updated in April 2026, contains the meta-analysis results. It reports performance metrics like a mean Dice coefficient of 89.2% for false lumen segmentation and pooled sensitivity of 0.94 for CT-based diagnosis.
48 studies on deep learning models for aortic dissection were analyzed, with 28 focused on segmentation and 20 on diagnosis. The review, authored by Yichen Zhao and last updated in April 2026, reports a mean Dice coefficient of 89.2% for false lumen segmentation and pooled sensitivity of 0.94 for CT-based diagnostic models. It concludes these models perform comparably to or better than clinicians, supporting their potential as clinical assistive tools.
A systematic review and meta-analysis of 48 studies published up to November 3, 2024, evaluating image-based deep learning models for aortic dissection. The document, authored by Yichen Zhao and last updated in April 2026, reports performance metrics including Dice coefficients for segmentation and pooled sensitivity/specificity for diagnosis across CT, CTA, and ECG modalities.
A dual-center study from Xuzhou, China, developed a CT-based radiomics model to predict pain relief after palliative radiotherapy for bone metastases. The dataset includes 134 patients from two hospitals, with clinical variables and radiomic features used to train 11 machine learning classifiers. The optimal k-nearest neighbors model achieved an AUC of 0.818 on an external test set.
134 patient records from a dual-center study conducted between January 2022 and December 2024. The dataset was created by Zhiling Wan to develop and validate a CT-based radiomics model for predicting pain relief after palliative radiotherapy. It includes clinical variables and radiomic features, with results showing a k-nearest neighbors model achieving an AUC of up to 0.823.
Approximately 99,000 H&E-stained histopathological image patches from the GasHisSDB and GCHTID datasets, used to train and validate a multi-task deep learning model. The dataset was created by Qing-Chun Feng and last updated on 2026-04-21. The model achieved a classification F1-score of 0.938 and a segmentation Dice coefficient of 0.839 on the test set.
Approximately 99,000 H&E-stained histopathological image patches from the GasHisSDB and GCHTID datasets, used to train a multi-task deep learning model for gastrointestinal cancer analysis. The dataset was created by Qing-Chun Feng and last updated in April 2026. Model validation achieved a classification F1-score of 0.938 and a segmentation Dice coefficient of 0.839.
Qing-Chun Feng's research dataset, last updated April 2026, contains approximately 99,000 H&E-stained histopathological image patches from the GasHisSDB and GCHTID datasets. The data underpins a study developing a multi-task deep learning framework for classification and segmentation of tumor and microenvironment structures in gastrointestinal cancer. The framework achieved a classification F1-score of 0.938 and a segmentation Dice coefficient of 0.839 on the test set.
571,669 matched patient pairs from the TriNetX network were analyzed over a median follow-up of 6.8 years. This retrospective cohort study by Kuo-Chuan Hung, last updated in 2026, found a dose-dependent association between pre-diagnostic vitamin D deficiency and increased thyroid cancer risk. The analysis used propensity score matching on demographics, comorbidities, and laboratory values to control for confounding.