AIMC Topic: Retrospective Studies

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Lower bleeding volume contributes to decreasing surgical site infection in radical cystectomy: A propensity score-matched comparison of open versus robot-assisted radical cystectomy.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVES: To compare the incidence of surgical site infections (SSI) between robot-assisted and open radical cystectomies and investigate the risk factors for SSI after radical cystectomies.

Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: The present study compares the diagnostic performance of unenhanced computed tomography (CT) radiomics-based machine learning (ML) classifiers and a radiologist in cystic renal masses (CRMs).

Challenge in optimizing robotic pancreaticoduodenectomy including nerve plexus hanging maneuver: a Japanese single center experience of 76 cases.

Surgical endoscopy
BACKGROUND: Robotic pancreaticoduodenectomy (RPD) is technically demanding, and 20-50 cases are required to surpass the learning curve. This study aimed to show our experience of 76 cases from the introduction of RPD and report the changes in surgica...

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

European radiology experimental
BACKGROUND: To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation...

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tom...

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination.

BMC gastroenterology
BACKGROUND: Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) sys...

Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation.

PeerJ
OBJECTIVE: To evaluate the diagnostic value of artificial intelligence (AI) in the detection and management of benign and malignant pulmonary nodules (PNs) using computed tomography (CT) density.

Symptomatic Lymphocele After Robot-Assisted Pelvic Lymphadenectomy as Part of the Primary Surgical Treatment for Cervical and Endometrial Cancer: A Retrospective Cohort Study.

Journal of minimally invasive gynecology
STUDY OBJECTIVES: Pelvic lymph node dissection (PLND) is part of the primary treatment for early-stage cervical cancer and high-intermediate risk or high-risk endometrial cancer. Pelvic lymphocele is a postoperative complication of PLND, and when sym...

Application of Machine Learning Techniques to Development of Emergency Medical Rapid Triage Prediction Models in Acute Care.

Computers, informatics, nursing : CIN
Given the critical and complex features of medical emergencies, it is essential to develop models that enable prompt and suitable clinical decision-making based on considerable information. Emergency nurses are responsible for categorizing and priori...

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data.

Journal of endocrinological investigation
OBJECTIVE: To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of h...