AIMC Topic: Retrospective Studies

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Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study.

Clinical neurology and neurosurgery
OBJECTIVES: Ki67 is an important biomarker of pituitary adenoma (PA) aggressiveness. In this study, PA invasion of surrounding structures is investigated and deep learning (DL) models are established for preoperative prediction of Ki67 labeling index...

Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study.

Scientific reports
This retrospective study aimed to develop and validate a deep learning model for the classification of coronavirus disease-2019 (COVID-19) pneumonia, non-COVID-19 pneumonia, and the healthy using chest X-ray (CXR) images. One private and two public d...

Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

Drug safety
INTRODUCTION: Signal validation in pharmacovigilance is the process of evaluating data to decide whether evidence is sufficient to justify further assessment of a detected signal. During the signal validation process, safety experts in our organizati...

Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC using CT texture analysis: preliminary results.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Automatic segmentation has recently been developed to yield objective data. Prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using radiomics has been reported.

A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences.

The Laryngoscope
OBJECTIVE: To design a deep learning model based on multimodal magnetic resonance image (MRI) sequences for automatic parotid neoplasm classification, and to improve the diagnostic decision-making in clinical settings.

The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in ...

Robotic Revision of Hepaticojejunostomy for Benign Biliary Stricture.

The American surgeon
Surgical revision of biliary enteric anastomoses (BEA) can be a challenging undertaking and a robotic platform may provide advantages that address many of the technical obstacles. We present our technical approach and outcomes for patients undergoing...

[The role of bile acid measurement in the management of intrahepatic cholestasis of pregnancy].

Orvosi hetilap
Introduction: Intrahepatic cholestasis of pregnancy complicates 1% of pregnancies. It increases the risk of severe fetal complications significantly, including preterm delivery and stillbirth. Objective: To summarize our experience with serum total b...

Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep-learning features of primary lesions and peripheral lymph nodes.

International journal of cancer
Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In our study, we developed a prognostic prediction model for stage II CRC ...