AIMC Topic: Retrospective Studies

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Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing.

Journal of biomedical informatics
OBJECTIVE: Social determinants of health (SDOH) are non-medical factors that can profoundly impact patient health outcomes. However, SDOH are rarely available in structured electronic health record (EHR) data such as diagnosis codes, and more commonl...

Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning.

Scientific reports
Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients ...

Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network.

Scientific reports
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs...

Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease.

Respiratory research
BACKGROUND: The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3‑ and 5-year mortality by using machine ...

FDA-approved deep learning software application versus radiologists with different levels of expertise: detection of intracranial hemorrhage in a retrospective single-center study.

Neuroradiology
PURPOSE: To assess an FDA-approved and CE-certified deep learning (DL) software application compared to the performance of human radiologists in detecting intracranial hemorrhages (ICH).

Long-Term Oncological and Functional Outcomes After Robot-Assisted Partial Nephrectomy for Clinically Localized Renal Cell Carcinoma.

Annals of surgical oncology
BACKGROUND: To evaluate long-term oncological and renal function outcomes in patients treated with robot-assisted partial nephrectomy (RAPN) for renal cell carcinoma (RCC).

Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.

Thoracic cancer
BACKGROUND: Early identification of the malignant propensity of pulmonary ground-glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning-based...

Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.

Medical physics
BACKGROUND: A tomographic patient model is essential for radiation dose modulation in x-ray computed tomography (CT). Currently, two-view scout images (also known as topograms) are used to estimate patient models with relatively uniform attenuation c...

Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study.

Ophthalmology. Retina
PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus photographs (CFP), for which the reference standard for retinal thickness and fluid presence is derive...

Robotic deep inferior epigastric perforator flap harvest in breast reconstruction.

Microsurgery
INTRODUCTION: Reducing donor site morbidity after deep inferior epigastric artery perforator (DIEP) flap harvest relies mainly upon maintaining integrity of the anterior rectus sheath fascia. The purpose of this study is to describe our minimally-inv...