AI Medical Compendium Topic:
Reproducibility of Results

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Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.

European radiology
OBJECTIVES: This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions.

Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.

Magma (New York, N.Y.)
OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reco...

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival.

BMC medical research methodology
Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across variou...

Detecting pediatric appendicular fractures using artificial intelligence.

Revista da Associacao Medica Brasileira (1992)
OBJECTIVE: The primary objective was to assess the diagnostic accuracy of a deep learning-based artificial intelligence model for the detection of acute appendicular fractures in pediatric patients presenting with a recent history of trauma to the em...

From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing.

Journal of medical imaging and radiation sciences
BACKGROUND: The reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore "federated testing"...

SAD: semi-supervised automatic detection of BOLD activations in high temporal resolution fMRI data.

Magma (New York, N.Y.)
OBJECTIVE: Despite the prevalent use of the general linear model (GLM) in fMRI data analysis, assuming a pre-defined hemodynamic response function (HRF) for all voxels can lead to reduced reliability and may distort the inferences derived from it. To...

Assessment of inter-rater and intra-rater reliability of the Luna EMG robot as a tool for assessing upper limb proprioception in patients with stroke-a prospective observational study.

PeerJ
BACKGROUND: The aim of the study was to assess the inter-rater and intra-rater agreement of measurements performed with the Luna EMG (electromyography) multifunctional robot, a tool for evaluation of upper limb proprioception in individuals with stro...

Deep Learning-Enhanced Accelerated 2D TSE and 3D Superresolution Dixon TSE for Rapid Comprehensive Knee Joint Assessment.

Investigative radiology
OBJECTIVES: The aim of this study was to evaluate the use of a multicontrast deep learning (DL)-reconstructed 4-fold accelerated 2-dimensional (2D) turbo spin echo (TSE) protocol and the feasibility of 3-dimensional (3D) superresolution reconstructio...

Discrepancies in ASPECTS obtained by artificial intelligence and experts: Associated factors and prognostic implications.

European journal of radiology
PURPOSE: The differences between the Alberta Stroke Program Early CT Score (ASPECTS) obtained by experts and artificial intelligence (AI) software require elucidation. We aimed to characterize the discrepancies between the ASPECTS obtained by AI and ...

The simplification of the symptom Checklist-90 scale utilizing machine learning techniques.

Journal of affective disorders
The Symptom Checklist-90 (SCL-90), widely utilized for psychological assessments, faces challenges due to its extensive nature. Streamlining the SCL-90 is essential in order to enhance its practicality without compromising its broad applicability acr...