AIMC Topic: Retrospective Studies

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A radiomics feature-based machine learning models to detect brainstem infarction (RMEBI) may enable early diagnosis in non-contrast enhanced CT.

European radiology
OBJECTIVES: Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction (EBI). However, MRI is not practical for all patients who present with possible stroke and would lead to delayed treatment. The detection rate of EBI ...

Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning.

Ophthalmic research
INTRODUCTION: Development and validation of a deep learning algorithm to automatically identify and locate epiretinal memberane (ERM) regions in OCT images.

T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
OBJECTIVES: This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmo...

Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists.

Pediatric radiology
BACKGROUND: As the number of conventional radiographic examinations in pediatric emergency departments increases, so, too, does the number of reading errors by radiologists.

Improving breast cancer diagnostics with deep learning for MRI.

Science translational medicine
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast...

Generalizability and Bias in a Deep Learning Pediatric Bone Age Prediction Model Using Hand Radiographs.

Radiology
Background Although deep learning (DL) models have demonstrated expert-level ability for pediatric bone age prediction, they have shown poor generalizability and bias in other use cases. Purpose To quantify generalizability and bias in a bone age DL ...

Robot-Assisted Partial Nephrectomy with a New Robotic Surgical System: Feasibility and Perioperative Outcomes.

Journal of endourology
The aim of this study was to evaluate the feasibility and safety of a novel robotic system (KD-SR-01) for partial nephrectomy. Seventeen patients with small renal masses (SRMs) (≤4 cm) underwent KD-SR-01 robotic partial nephrectomy (KD-RPN) from De...

Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning.

Magnetic resonance imaging
BACKGROUND AND OBJECTIVES: Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive l...

Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort.

International journal of environmental research and public health
The selection of patients for the constitution of a cohort is a major issue for clinical research (prospective studies and retrospective studies in real life). Our objective was to validate in real life conditions the use of a Deep Learning process b...