What question did this study set out to answer?

The aim is to evaluate the effectiveness of machine learning models in classifying pulmonary nodules as benign or malignant.

May 14, 20260 citationsOpen Access

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Diagnostic Accuracy of Machine Learning for Classifying Benign and Malignant Pulmonary Nodules — A Systematic Review and Diagnostic Test Accuracy Meta-analysis

AZAfshin Zare ATAmin TamadonWest Kazakhstan Marat Ospanov State Medical University

Key Points

The aim is to evaluate the effectiveness of machine learning models in classifying pulmonary nodules as benign or malignant.
Conducted a systematic review and DTA meta-analysis assessing machine learning and deep learning models.
Employed QUADAS-2 for risk-of-bias assessment and followed PRISMA 2020 guidelines.
Planned statistical analyses included pooled sensitivity, specificity, and SROC analyses.
To be determined based on subsequent analyses of included studies.
Expected outcomes include calculated pooled sensitivity and specificity across machine learning models.

Abstract

This component contains the study protocol for a systematic review and diagnostic test accuracy (DTA) meta-analysis evaluating the performance of machine learning and deep learning models for differentiating benign and malignant pulmonary nodules. The protocol describes the research objectives, eligibility criteria, search strategy, study selection process, data extraction, risk-of-bias assessment using QUADAS-2, and planned statistical analyses including pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and summary receiver operating characteristic (SROC) analyses. The review will follow PRISMA 2020 and PRISMA-DTA reporting guidelines.

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Diagnostic Accuracy of Machine Learning for Classifying Benign and Malignant Pulmonary Nodules — A Systematic Review and Diagnostic Test Accuracy Meta-analysis

Key Points

Abstract

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