AIMC Topic: Retrospective Studies

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Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach.

European radiology experimental
BACKGROUND: To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT).

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome.

PloS one
State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANN...

Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods.

Skeletal radiology
OBJECTIVE: The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in the diagnosis of hip osteoarthritis, and plain pelvic radiography is the first preferred imaging me...

Feasibility and safety of robot-assisted bariatric conversions and revisions.

Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery
BACKGROUND: Conversions and revisions after bariatric procedures are inevitable and associated with longer operative time and higher complication rates. Because robot-assisted procedures allow better dissection, robotic conversions and revisions may ...

Artificial intelligence to detect tympanic membrane perforations.

The Journal of laryngology and otology
OBJECTIVE: To explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings.

Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: The prognosis for pharyngeal cancer is relatively poor. It is usually diagnosed in an advanced stage. Although the recent development of narrow-band imaging (NBI) and increased awareness among endoscopists have enabled detection of superf...

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans.

The Journal of laryngology and otology
OBJECTIVE: Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algori...

Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma.

Journal of magnetic resonance imaging : JMRI
UNLABELLED: Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making.

Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.

European radiology
OBJECTIVES: We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods.

Comparison of Robot-Assisted and Laparoscopic Partial Nephrectomy for Completely Endophytic Renal Tumors: A High-Volume Center Experience.

Journal of endourology
To compare the perioperative, functional, and oncologic outcomes of robot-assisted partial nephrectomy (RAPN) and laparoscopic partial nephrectomy (LPN) for completely endophytic renal tumors (three points for the "E" element of the R.E.N.A.L. scori...