AIMC Topic: Retrospective Studies

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Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis.

Scientific reports
We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort ...

Prognostic prediction in soft-tissue sarcomas using deep learning and digital pathology of tumor and margin areas.

Scientific reports
The histological FNCLCC grade is the primary prognostic factor in soft-tissue sarcoma (STS) but fails to fully capture high risk patients. This study aimed to develop and validate a deep learning (DL) model to predict metastatic relapse-free survival...

Interpretable radiomics-based machine learning model for differentiating glioblastoma from primary central nervous system lymphoma using contrast-enhanced T1-weighted imaging.

Scientific reports
This study aimed to develop and validate an interpretable radiomics-based machine learning model using contrast-enhanced T1-weighted imaging (CE-T1WI) to differentiate glioblastoma (GB) from primary central nervous system lymphoma (PCNSL), while comp...

Deep learning-based non-invasive differential diagnosis of eyelid basal cell and sebaceous gland carcinomas using photographic images.

International ophthalmology
PURPOSE: Pathological examination, the current gold standard for differentiating eyelid basal cell carcinoma (BCC) and sebaceous gland carcinoma (SGC), is invasive, time-consuming, and often inaccessible in primary care hospitals. Therefore, a non-in...

Robot or human? Manoeuvring switching intention after robot service failure.

PloS one
This study attempts to scrutinise tourists' switching intentions towards human service after a robot service failure, with the zone of tolerance and trust on stance in technology as moderators. The study adopts the unified theory of acceptance and us...

Interpretable machine learning model for predicting low birth weight in singleton pregnancies: a retrospective cohort study.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW), defined as a newborn weighing less than 2500 g, is an increasingly significant public health concern. Exploring the risk and protective factors for LBW is getting more and more important. This study aimed to utiliz...

Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.

Scientific reports
Abdominal aortic aneurysm (AAA) progression carries a significant rupture risk, demanding accurate prediction models beyond traditional methods that rely on limited clinical parameters and often overlook complex factor interplay. We aimed to enhance ...

AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer.

Nature communications
Risk stratification remains a critical challenge in non-small cell lung cancer patients for optimal therapy selection. In this study, we develop an artificial intelligence-powered spatial cellomics approach that combines histology, multiplex immunofl...

Predicting Postoperative Stress Urinary Incontinence After Prolapse Surgery via Machine Learning and Regression Models: Development and Validation Study.

JMIR medical informatics
BACKGROUND: Pelvic organ prolapse (POP) and stress urinary incontinence (SUI) often concurrently exist. The incontinence in some patients with POP resolves after POP surgery, but it persists in others. Some patients without SUI before surgery may dev...

MRI-Based Quantification of Intratumoral Heterogeneity for Predicting Progression-Free Survival in Patients with Lung Cancer Brain Metastasis Receiving Radiotherapy.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Our aim was to investigate the potential of using MRI-based habitat features for predicting progression-free survival (PFS) in patients with lung cancer brain metastasis (LCBM) receiving radiotherapy.