Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease
| Autor: | Basel Yacoub, Natalie Stringer, Madalyn Snoddy, Danielle M. Dargis, Ismail Kabakus, U. Joseph Schoepf, Gilberto J. Aquino, Vincenzo Vingiani, Jeremy R. Burt, Philipp Hoelzer, Andres F. Abadia, Pooyan Sahbaee, Jonathan I. Sperl, Madison Kocher, Megan Mercer |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Pulmonary and Respiratory Medicine
Lung Neoplasms Lung medicine.diagnostic_test business.industry Solitary Pulmonary Nodule Group assessment Computed tomography Diagnostic accuracy Sensitivity and Specificity medicine.anatomical_structure Patient Load Artificial Intelligence Lung disease Humans Medicine Detection performance Radiology Nuclear Medicine and imaging In patient Artificial intelligence business Retrospective Studies |
| Zdroj: | Journal of Thoracic Imaging. 37:154-161 |
| ISSN: | 0883-5993 |
| DOI: | 10.1097/rti.0000000000000613 |
| Popis: | OBJECTIVES The aim of the study is to investigate the performance of artificial intelligence (AI) convolutional neural networks (CNN) in detecting lung nodules on chest computed tomography of patients with complex lung disease, and demonstrate its noninferiority when compared against an experienced radiologist through clinically relevant assessments. METHODS A CNN prototype was used to retrospectively evaluate 103 complex lung disease cases and 40 control cases without reported nodules. Computed tomography scans were blindly evaluated by an expert thoracic radiologist; a month after initial analyses, 20 positive cases were re-evaluated with the assistance of AI. For clinically relevant applications: (1) AI was asked to classify each patient into nodules present or absent and (2) AI results were compared against standard radiology reports. Standard statistics were performed to determine detection performance. RESULTS AI was, on average, 27 seconds faster than the expert and detected 8.4% of nodules that would have been missed. AI had a sensitivity of 67.7%, similar to an accuracy reported for experienced radiologists. AI correctly classified each patient (nodules present/absent) with a sensitivity of 96.1%. When matched against radiology reports, AI performed with a sensitivity of 89.4%. Control group assessment demonstrated an overall specificity of 82.5%. When aided by AI, the expert decreased the average assessment time per case from 2:44 minutes to 35.7 seconds, while reporting an overall increase in confidence. CONCLUSION In a group of patients with complex lung disease, the sensitivity of AI is similar to an experienced radiologist and the tool helps detect previously missed nodules. AI also helps experts analyze for lung nodules faster and more confidently, a feature that is beneficial to patients and favorable to hospitals due to increased patient load and need for shorter turnaround times. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|