AIMC Topic: Retrospective Studies
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Novel risk factors and personalized risk calculator for predicting proximal junctional kyphosis after adult spinal deformity surgery.
The bone & joint journal
Aug 1, 2025
AIMS: Proximal junctional kyphosis (PJK) is a prevalent and detrimental complication associated with corrective surgery for adult spinal deformity (ASD). While existing predictive models have been able to predict PJK, they have lacked individualized ...
An AI-driven machine learning approach identifies risk factors associated with 30-day mortality following total aortic arch replacement combined with stent elephant implantation.
Annals of medicine
Jul 31, 2025
OBJECTIVES: During emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavourable outcomes throughout their hospital stay. The combination of total aortic arch replacement (TAR) and frozen elephant trunk (FET) implant...
IgCONDA-PET: Weakly-supervised PET anomaly detection using implicitly-guided attention-conditional counterfactual diffusion modeling - a multi-center, multi-cancer, and multi-tracer study.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 31, 2025
Minimizing the need for pixel-level annotated data to train PET lesion detection and segmentation networks is highly desired and can be transformative, given time and cost constraints associated with expert annotations. Current unsupervised or weakly...
Advanced multi-label brain hemorrhage segmentation using an attention-based residual U-Net model.
BMC medical informatics and decision making
Jul 31, 2025
OBJECTIVE: This study aimed to develop and assess an advanced Attention-Based Residual U-Net (ResUNet) model for accurately segmenting different types of brain hemorrhages from CT images. The goal was to overcome the limitations of manual segmentatio...
A retrospective cohort study using machine learning to predict coronary artery lesions in children with Kawasaki disease.
BMC pediatrics
Jul 31, 2025
BACKGROUND: Kawasaki disease (KD) mainly occurs in children under 5 years old, and the most common complication of KD is coronary artery lesion (CAL). In recent years, the incidence rate of KD has increased year by year worldwide, so it is particular...
Retrospective study of onychomycosis patients treated with ciclopirox 8% HPCH and oral antifungals applying artificial intelligence to electronic health records.
Scientific reports
Jul 31, 2025
We conducted a multicenter retrospective analysis of 408 patients diagnosed with onychomycosis who attended three tertiary care Spanish hospitals. The study was conducted to assess the clinical characteristics and outcomes of onychomycosis patients u...
Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data.
Scientific reports
Jul 31, 2025
Myocardial infarction (MI) remains one of the greatest contributors to mortality, and patients admitted to the intensive care unit (ICU) with myocardial infarction are at higher risk of death. In this study, we use two retrospective cohorts extracted...
Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal cancer: a preliminary study.
Radiation oncology (London, England)
Jul 30, 2025
BACKGROUND: This study aimed to develop and validate a multi-temporal magnetic resonance imaging (MRI)-based delta-radiomics model to accurately predict severe acute radiation enteritis risk in patients undergoing total neoadjuvant therapy (TNT) for ...
A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion.
BMC medical imaging
Jul 30, 2025
OBJECTIVES: Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume ...
Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.
Scientific reports
Jul 30, 2025
The purpose of this study was to create and validate an ultrasound-based graph convolutional network (US-based GCN) model for the prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. A total of 820 eligible patients wit...
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