AIMC Topic: Retrospective Studies

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Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program.

Radiology
Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commerc...

Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression.

Journal of computer-aided molecular design
The retrospective evaluation of virtual screening approaches and activity prediction models are important for methodological development. However, for fair comparison, evaluation data sets must be carefully prepared. In this research, we compiled str...

The effect of preoperative membranous urethral length on likelihood of postoperative urinary incontinence after robot-assisted radical prostatectomy.

Prostate cancer and prostatic diseases
BACKGROUND: Urinary incontinence after radical prostatectomy affects many men. In addition to surgical and patient factors, longer preoperative membranous urethral length (MUL) has been suggested to be associated with improved postoperative urinary c...

Measuring the critical shoulder angle on radiographs: an accurate and repeatable deep learning model.

Skeletal radiology
PURPOSE: Since the critical shoulder angle (CSA) is considered a risk factor for shoulder pathology and the intra- and inter-rater variabilities in its calculation are not negligible, we developed a deep learning model that calculates it automaticall...

Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images.

Hepatology international
BACKGROUND: Microvascular invasion (MVI) is essential for the management of hepatocellular carcinoma (HCC). However, MVI is hard to evaluate in patients without sufficient peri-tumoral tissue samples, which account for over a half of HCC patients.

The efficacy of F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

The British journal of radiology
OBJECTIVE: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumor...

Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods.

Joint diseases and related surgery
OBJECTIVES: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology.

A deep learning algorithm to improve readers' interpretation and speed of pancreatic cystic lesions on dual-phase enhanced CT.

Abdominal radiology (New York)
PURPOSE: To develop a deep learning model (DLM) to improve readers' interpretation and speed in the differentiation of pancreatic cystic lesions (PCLs) on dual-phase enhanced CT, and a low contrast media dose, external testing set validated the model...

The Impact of Postoperative Renal Function Recovery after Laparoscopic and Robot-Assisted Partial Nephrectomy in Patients with Renal Cell Carcinoma.

Medicina (Kaunas, Lithuania)
Background and objectives: This study aimed to evaluate the association between warm ischemic time (WIT) and postoperative renal function using Trifecta achievement in patients with renal cell carcinoma (RCC) who underwent robotic (RAPN) or laparosco...