AIMC Topic: Reproducibility of Results

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Deep learning prediction of survival in patients with heart failure using chest radiographs.

The international journal of cardiovascular imaging Jul 5, 2024
Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and val...
Humans Reproducibility of Results Risk Factors Aged Aged, 80 and over Middle Aged Male Decision Support Techniques Risk Assessment Heart Failure Prognosis Deep Learning Retrospective Studies Female Radiography, Thoracic Time Factors Predictive Value of Tests
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Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

Frontiers in immunology Jul 5, 2024
BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning sys...
Graft Rejection Adult Biopsy Male Allografts Middle Aged Humans Deep Learning Kidney Kidney Transplantation Female Reproducibility of Results
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External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.

Journal of nephrology Jul 4, 2024
BACKGROUND: Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to ...
ROC Curve Renal Insufficiency, Chronic Predictive Value of Tests Area Under Curve Cause of Death Male Middle Aged Risk Assessment Severity of Illness Index Time Factors Female Reproducibility of Results Risk Factors Aged Humans Machine Learning Prognosis
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Development and validation of a deep learning-based method for automatic measurement of uterus, fibroid, and ablated volume in MRI after MR-HIFU treatment of uterine fibroids.

European journal of radiology Jul 3, 2024
INTRODUCTION: The non-perfused volume divided by total fibroid load (NPV/TFL) is a predictive outcome parameter for MRI-guided high-intensity focused ultrasound (MR-HIFU) treatments of uterine fibroids, which is related to long-term symptom relief. I...
Tumor Burden Female Humans Treatment Outcome Reproducibility of Results Leiomyoma Magnetic Resonance Imaging Adult Magnetic Resonance Imaging, Interventional Uterus Middle Aged High-Intensity Focused Ultrasound Ablation Uterine Neoplasms Deep Learning
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Characterizing Sentinel Lymph Node Status in Breast Cancer Patients Using a Deep-Learning Model Compared With Radiologists' Analysis of Grayscale Ultrasound and Lymphosonography.

Ultrasound quarterly Jul 3, 2024
The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enro...
Reproducibility of Results Aged Adult Breast Neoplasms Middle Aged Humans Female Radiologists Ultrasonography, Mammary Breast Deep Learning Sentinel Lymph Node Sentinel Lymph Node Biopsy Ultrasonography Lymphatic Metastasis Contrast Media
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Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning.

JACC. Cardiovascular interventions Jul 3, 2024
BACKGROUND: There is limited data on predicting successful chronic total occlusion crossing using primary antegrade wiring (AW).
Middle Aged Time Factors Treatment Outcome Chronic Disease Predictive Value of Tests Registries Percutaneous Coronary Intervention Coronary Occlusion Female Humans Reproducibility of Results Machine Learning Risk Factors Decision Support Techniques Aged Male
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Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine Jul 2, 2024
PURPOSE: We developed new deep learning-based hierarchical brain segmentation (DLHBS) method that can segment T1-weighted MR images (T1WI) into 107 brain subregions and calculate the volume of each subregion. This study aimed to evaluate the repeatab...
Humans Brain Female Reproducibility of Results Imaging, Three-Dimensional Image Processing, Computer-Assisted Magnetic Resonance Imaging Gray Matter White Matter Young Adult Adult Male Middle Aged Deep Learning
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Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2): International Improvement and Validation Study.

The American journal of surgical pathology Jul 2, 2024
Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quant...
Neoplasm, Residual Humans Neoadjuvant Therapy Artificial Intelligence Pancreatic Neoplasms Female Reproducibility of Results Male Predictive Value of Tests Image Interpretation, Computer-Assisted Pancreatectomy
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Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Jul 2, 2024
OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorit...
Middle Aged Diagnosis, Computer-Assisted Support Vector Machine Venous Thrombosis Predictive Value of Tests Reproducibility of Results Aged Adult Male Registries Iran Intracranial Thrombosis Humans Machine Learning Retrospective Studies Female
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Measuring Vertical Jump Height With Artificial Intelligence Through a Cell Phone: A Validity and Reliability Report.

Journal of strength and conditioning research Jul 2, 2024
Erik, HT, Onn, SW, and Montalvo, S. Vertical jump height with artificial intelligence through a cell phone: a validity and reliability report. J Strength Cond Res 38(9): e529-e533, 2024-This study estimated the reliability and validity of an artifici...
Cell Phone Mobile Applications Exercise Test Athletic Performance Humans Athletes Artificial Intelligence Female Reproducibility of Results Young Adult Adult Cross-Sectional Studies Male Adolescent
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