AIMC Topic: Retrospective Studies

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Deep Learning Body Region Classification of MRI and CT Examinations.

Journal of digital imaging
This study demonstrates the high performance of deep learning in identification of body regions covering the entire human body from magnetic resonance (MR) and computed tomography (CT) axial images across diverse acquisition protocols and modality ma...

Elective robotic partial colon and rectal resections: series of 170 consecutive robot procedures involving the Da Vinci Xi robot by a community general surgeon.

Journal of robotic surgery
Robotic colorectal procedures may overcome the disadvantages of laparoscopic surgery. While the literature has multiple studies from specialized centers, experience from general surgeons is minimal. The purpose of this case series is to review electi...

Robot-assisted retroperitoneal lymph node dissection: a systematic review of perioperative outcomes.

BJU international
OBJECTIVE: To assess the safety and feasibility of robot-assisted retroperitoneal lymph node dissection (R-RPLND) and to compare the perioperative outcomes of R-RPLND with open RPLND (O-RPLND), as RPLND forms an integral part of the management of tes...

External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection.

World neurosurgery
BACKGROUND: Artificial intelligence applications have gained traction in the field of cerebrovascular disease by assisting in the triage, classification, and prognostication of both ischemic and hemorrhagic stroke. The Caire ICH system aims to be the...

Non-invasive regional cerebral blood flow quantification in the 123I-IMP autoradiography using artificial neural network.

PloS one
PURPOSE: Regional cerebral blood flow (rCBF) quantification using 123I-N-isopropyl-p-iodoamphetamine (123I-IMP) requires an invasive, one-time-only arterial blood sampling for measuring the 123I-IMP arterial blood radioactivity concentration (Ca10). ...

Autonomous Chest Radiograph Reporting Using AI: Estimation of Clinical Impact.

Radiology
Background Automated interpretation of normal chest radiographs could alleviate the workload of radiologists. However, the performance of such an artificial intelligence (AI) tool compared with clinical radiology reports has not been established. Pur...

Deep Learning for Head and Neck CT Angiography: Stenosis and Plaque Classification.

Radiology
Background Studies have rarely investigated stenosis detection from head and neck CT angiography scans because accurate interpretation is time consuming and labor intensive. Purpose To develop an automated convolutional neural network-based method fo...

Artificial Intelligence Screening of Medical School Applications: Development and Validation of a Machine-Learning Algorithm.

Academic medicine : journal of the Association of American Medical Colleges
PURPOSE: To explore whether a machine-learning algorithm could accurately perform the initial screening of medical school applications.

FasterRib: A deep learning algorithm to automate identification and characterization of rib fractures on chest computed tomography scans.

The journal of trauma and acute care surgery
OBJECTIVE: Characterizing and enumerating rib fractures are critical to informing clinical decisions, yet in-depth characterization is rarely performed because of the manual burden of annotating these injuries on computed tomography (CT) scans. We hy...

Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children.

Pediatric radiology
BACKGROUND: Advances have been made in the use of artificial intelligence (AI) in the field of diagnostic imaging, particularly in the detection of fractures on conventional radiographs. Studies looking at the detection of fractures in the pediatric ...