AIMC Topic: Retrospective Studies

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Robotic and laparoscopic surgical procedures for colorectal cancer.

Journal of robotic surgery
The study aims to investigate perioperative indices and immediate outcomes of laparoscopic and robotic surgical interventions in colorectal cancer patients. The study included 163 patients [90 (55.2%) females and 73 (44.8%) males, aged 67.46 ± 6.72 y...

Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses.

Endoscopy
BACKGROUND : There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognition of data patterns. This study ai...

Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN.

European radiology
OBJECTIVE: To compare the utility of deep learning reconstruction (DLR) for improving acquisition time, image quality, and intraductal papillary mucinous neoplasm (IPMN) evaluation for 3D MRCP obtained with parallel imaging (PI), multiple k-space dat...

Oxygen delivery in pediatric cardiac surgery and its association with acute kidney injury using machine learning.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: Acute kidney injury (AKI) after pediatric cardiac surgery with cardiopulmonary bypass (CPB) is a frequently reported complication. In this study we aimed to determine the oxygen delivery indexed to body surface area (Doi) threshold associa...

Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

Academic radiology
RATIONALE AND OBJECTIVES: Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We develop...

A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.

European radiology
OBJECTIVES: To evaluate the value of deep learning (DL) combining multimodal radiomics and clinical and imaging features for differentiating ocular adnexal lymphoma (OAL) from idiopathic orbital inflammation (IOI).

A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study.

Oral oncology
OBJECTIVE: Tissue slides from Oral cavity squamous cell carcinoma (OC-SCC), particularly the epithelial regions, hold morphologic features that are both diagnostic and prognostic. Yet, previously developed approaches for automated epithelium segmenta...