AIMC Topic: Reproducibility of Results

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Enhancing mosquito classification through self-supervised learning.

Scientific reports
Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learnin...

Reliable and easy-to-use calculating tool for the Nail Psoriasis Severity Index using deep learning.

NPJ systems biology and applications
Since nail psoriasis restricts the patient's daily activities, therapeutic intervention based on reliable and reproducible evaluation is critical. The Nail Psoriasis Severity Index (NAPSI) is a validated scoring tool, but its usefulness is limited by...

Development and Validation of a Predictive Model for Maternal Cardiovascular Morbidity Events in Patients With Hypertensive Disorders of Pregnancy.

Anesthesia and analgesia
BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity, mortality, and accelerated cardiovascular (CV) disease. Comorbid conditions are likely important predictors of CV risk in pregnant people. Currently,...

Utilization of Artificial Intelligence in the Creation of Patient Information on Laryngology Topics.

The Laryngoscope
OBJECTIVE: To evaluate and compare the readability and quality of patient information generated by Chat-Generative Pre-Trained Transformer-3.5 (ChatGPT) and the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) using validated instru...

Artificial intelligence based assessment of minimally invasive surgical skills using standardised objective metrics - A narrative review.

American journal of surgery
INTRODUCTION: Many studies display significant heterogeneity in the reliability of artificial intelligence (AI) assessment of minimally invasive surgical (MIS) skills. Our objective is to investigate whether AI systems utilising standardised objectiv...

Automated detection of bone lesions using CT and MRI: a systematic review.

La Radiologia medica
PURPOSE: The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, focusing on advancements in artificial intelligence (AI) applications.

Autonomous mobile robots for exploratory synthetic chemistry.

Nature
Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making. Most autonomous laboratories involve bespoke automated equipment, and reaction outcomes are ofte...

Development of a Dual-Plane MRI-Based Deep Learning Model to Assess the 1-Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Tubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has shown promise, its application in evaluating postoperative outcomes in TMD has not been fully explor...

Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method.

European radiology
OBJECTIVE: To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs).

A critical comparative study of the performance of three AI-assisted programs for bone age determination.

European radiology
OBJECTIVES: To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&P). Therefore, the current study aimed to compare the performance of th...