AIMC Topic: Retrospective Studies
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Real-world data of CanAssist Breast- first immunohistochemistry and AI-based prognostic test.
Scientific reports
Aug 19, 2025
CanAssist Breast (CAB), an immunohistochemistry (IHC) and artificial intelligence-based prognostic test, was developed on Hormone receptor-positive (HR +), HER2/neu-negative (HER2-) breast tumors from Indian patients and validated in retrospective gl...
Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study.
BMJ open
Aug 19, 2025
OBJECTIVES: This study aims to develop a deep learning algorithm (DLA) using the InceptionV3 architecture for effective diabetic peripheral neuropathy (DPN) screening via corneal confocal microscopy (CCM) images.
Robotic arm vs. stereotactic frame in deep brain stimulation surgery for movement disorders: a retrospective cohort study.
Acta neurochirurgica
Aug 12, 2025
BACKGROUND: Recently, robotic arms have been incorporated into the implantation of electrodes for deep brain stimulation (DBS).This study aimed to determine the accuracy of brain electrode placement, initial clinical efficacy, and safety profile of t...
MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach.
European radiology experimental
Aug 12, 2025
BACKGROUND: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and ...
Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.
Journal of translational medicine
Aug 11, 2025
OBJECTIVES: This study aims to integrate CT imaging with occupational health surveillance data to construct a multimodal model for preclinical CWP identification and individualized risk evaluation.
An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study.
Journal of translational medicine
Aug 11, 2025
OBJECTIVE: To develop explainable machine learning models that integrate multimodal imaging and pathological biomarkers to predict axillary lymph node metastasis (ALNM) in breast cancer patients and assess their clinical utility.
Early detection of vascular catheter-associated infections employing supervised machine learning - a case study in Lleida region.
BMC medical informatics and decision making
Aug 11, 2025
Healthcare-associated infections (HAIs), particularly Vascular Catheter-Associated Infections (VCAIs), are a significant concern, accounting for over 7% of all infections and are often linked to medical devices. Early detection of VCAIs before invasi...
Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.
BMC medical imaging
Aug 11, 2025
BACKGROUND: Gliomas exhibit a high recurrence rate, particularly in the peritumoural brain zone after surgery. This study aims to develop and validate a radiomics-based model using preoperative fluid-attenuated inversion recovery (FLAIR) and T1-weigh...
Artificial intelligence-assisted identification of condensing osteitis and idiopathic osteosclerosis on panoramic radiographs.
Scientific reports
Aug 11, 2025
Idiopathic osteosclerosis (IOS) and condensing osteitis (CO) represent radiopaque lesions often detected incidentally within the jaws, posing substantial diagnostic challenges due to their overlapping radiographic characteristics. The objective of th...
18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.
Medical oncology (Northwood, London, England)
Aug 11, 2025
Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer. In this study, we developed deep radiomic models to enhance the prediction of chemotherapy response after...
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