Sample-size determination methodologies for machine learning in medical imaging research: a systematic review

I Balki, A Amirabadi, J Levman… - Canadian …, 2019 - journals.sagepub.com
Purpose The required training sample size for a particular machine learning (ML) model
applied to medical imaging data is often unknown. The purpose of this study was to provide …

[HTML][HTML] Radiomics and deep learning for disease detection in musculoskeletal radiology: an overview of novel MRI-and CT-based approaches

B Fritz, HY Paul, R Kijowski, J Fritz - Investigative radiology, 2023 - journals.lww.com
Radiomics and machine learning–based methods offer exciting opportunities for improving
diagnostic performance and efficiency in musculoskeletal radiology for various tasks …

Natural and artificial intelligence in neurosurgery: a systematic review

JT Senders, O Arnaout, AV Karhade… - …, 2018 - journals.lww.com
BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows
computer algorithms to learn from experience without being explicitly programmed …

Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images?

T Hodgdon, MDF McInnes, N Schieda, TA Flood… - Radiology, 2015 - pubs.rsna.org
Purpose To determine the accuracy of texture analysis to differentiate fat-poor
angiomyolipoma (fp-AML) from renal cell carcinoma (RCC) on unenhanced computed …

Machine learning in breast MRI

B Reig, L Heacock, KJ Geras… - Journal of magnetic …, 2020 - Wiley Online Library
Machine‐learning techniques have led to remarkable advances in data extraction and
analysis of medical imaging. Applications of machine learning to breast MRI continue to …

[HTML][HTML] Neurosurgery and artificial intelligence

M Mofatteh - AIMS neuroscience, 2021 - ncbi.nlm.nih.gov
Neurosurgeons receive extensive and lengthy training to equip themselves with various
technical skills, and neurosurgery require a great deal of pre-, intra-and postoperative …

Texture analysis as a radiomic marker for differentiating renal tumors

HS Yu, J Scalera, M Khalid, AS Touret, N Bloch… - Abdominal …, 2017 - Springer
Purpose To evaluate the utility of texture analysis for the differentiation of renal tumors,
including the various renal cell carcinoma subtypes and oncocytoma. Materials and …

Texture analysis and machine learning for detecting myocardial infarction in noncontrast low-dose computed tomography: unveiling the invisible

M Mannil, J von Spiczak, R Manka… - Investigative …, 2018 - journals.lww.com
Objectives The aim of this study was to test whether texture analysis and machine learning
enable the detection of myocardial infarction (MI) on non–contrast-enhanced low radiation …

Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review

L Alic, WJ Niessen, JF Veenland - PloS one, 2014 - journals.plos.org
Background Many techniques are proposed for the quantification of tumor heterogeneity as
an imaging biomarker for differentiation between tumor types, tumor grading, response …

Diagnostic accuracy of MRI texture analysis for grading gliomas

A Ditmer, B Zhang, T Shujaat, A Pavlina… - Journal of Neuro …, 2018 - Springer
Purpose Texture analysis (TA) can quantify variations in surface intensity or patterns,
including some that are imperceptible to the human visual system. The purpose of this study …