AIMC Topic: Retrospective Studies

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Predicting central choroidal thickness from colour fundus photographs using deep learning.

PloS one
The estimation of central choroidal thickness from colour fundus images can improve disease detection. We developed a deep learning method to estimate central choroidal thickness from colour fundus images at a single institution, using independent da...

Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and dissemin...

Prediction of hospital mortality among critically ill patients in a single centre in Asia: comparison of artificial neural networks and logistic regression-based model.

Hong Kong medical journal = Xianggang yi xue za zhi
INTRODUCTION: This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong ...

Correlative Assessment of Machine Learning-Based Cobb Angle Measurements and Human-Based Measurements in Adolescent Idiopathic and Congenital Scoliosis.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Scoliosis is a complex spine deformity with direct functional and cosmetic impacts on the individual. The reference standard for assessing scoliosis severity is the Cobb angle which is measured on radiographs by human specialists, carrying interobse...

Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.

European radiology
BACKGROUND: Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective.

Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting surgery in patients.

Clinical and experimental nephrology
OBJECTIVES: This study aimed to develop machine learning models for risk prediction of continuous renal replacement therapy (CRRT) following coronary artery bypass grafting (CABG) surgery in intensive care unit (ICU) patients.

Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening.

Japanese journal of radiology
PURPOSE: To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model.

Responsible Development of Emerging Technologies: Extensions and Lessons From Nanotechnology for Worker Protection.

Journal of occupational and environmental medicine
OBJECTIVES: This paper identifies approaches to the responsible development of emerging technologies to secure worker safety and health.