AIMC Topic: Reproducibility of Results

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Paving a Path to Clinical Impact with Radiomics: Enabling Reproducibility and Reach.

Cancer research
Radiomics, the extraction of quantitative data from images, holds promise for noninvasively characterizing tumor phenotypes. Tools like LIFEx have improved the accessibility, transparency, and reproducibility of radiomic feature extraction by offerin...

Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality.

Cancer research
The integration of artificial intelligence (AI) in cancer research has significantly advanced radiology, pathology, and multimodal approaches, offering unprecedented capabilities in image analysis, diagnosis, and treatment planning. AI techniques pro...

A novel deep learning system for automated diagnosis and grading of lumbar spinal stenosis based on spine MRI: model development and validation.

Neurosurgical focus
OBJECTIVE: The study aimed to develop a single-stage deep learning (DL) screening system for automated binary and multiclass grading of lumbar central stenosis (LCS), lateral recess stenosis (LRS), and lumbar foraminal stenosis (LFS).

External Validation of an Algorithm to Guide Opioid Administration at the End of Surgery-Protocol for an Observational Cohort Study of the OPIAID Algorithm.

Acta anaesthesiologica Scandinavica
BACKGROUND: Despite advances in pain management, inadequate pain relief and opioid-related adverse events remain common challenges in perioperative care, often contributing to prolonged recovery and reduced quality of life. The perioperative opioid a...

Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active Surveillance of Solid Small Renal Masses: A Multiobserver Study.

Radiology. Imaging cancer
Purpose To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans, with and without Digital Imaging and Communications in Medicine-based deep learning-based denoising (DLD), in evaluating small renal masses (SRM...

Integrating machine learning and reliability analysis: A novel approach to predicting heavy metal removal efficiency using biochar.

Ecotoxicology and environmental safety
Soil contamination with heavy metals (HMs) presents critical environmental and public health risks due to their long-term persistence and tendency to bioaccumulate. Biochar has gained recognition as an effective amendment for HM immobilization, owing...

Fast cortical thickness estimation using deep learning-based anatomy segmentation and diffeomorphic registration.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurately and efficiently estimating the cortical thickness from magnetic resonance images (MRIs) is crucial for neuroscientific studies and clinical applications with various large-scale datasets. Diffeomorphic registration-based cortical thickness...

Establishment of an intelligent analysis system for clinical image features of melanonychia based on deep learning image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Melanonychia, a condition that can be indicative of malignant melanoma, presents a significant challenge in early diagnosis due to the invasive nature and equipment dependency of traditional diagnostic methods such as nail biopsy and dermatoscope ima...