Large-Scale Study on AI's Impact on Identifying Chest Radiographs with No Actionable Disease in Outpatient Imaging.

Autor:	Mansoor A; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ. Electronic address: Awais.Mansoor@siemens-healthineers.com., Schmuecking I; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ., Ghesu FC; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ., Georgescu B; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ., Grbic S; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ., Vishwanath RS; Siemens Healthineers, Digital Technology and Innovation India, Bengaluru, India., Farri O; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ., Ghosh R; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ., Vunikili R; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ., Zimmermann M; Siemens Healthineers, Digital & Automation, Malvern, PA., Sutcliffe J; Zwanger-Pesiri Radiology, Lindenhurst, NY., Mendelsohn SL; Zwanger-Pesiri Radiology, Lindenhurst, NY., Comaniciu D; Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ., Gefter WB; Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, PA.
Jazyk:	angličtina
Zdroj:	Academic radiology [Acad Radiol] 2024 Dec; Vol. 31 (12), pp. 5300-5313. Date of Electronic Publication: 2024 Jul 12.
DOI:	10.1016/j.acra.2024.06.031
Abstrakt:	Rationale and Objectives: Given the high volume of chest radiographs, radiologists frequently encounter heavy workloads. In outpatient imaging, a substantial portion of chest radiographs show no actionable findings. Automatically identifying these cases could improve efficiency by facilitating shorter reading workflows. Purpose: A large-scale study to assess the performance of AI on identifying chest radiographs with no actionable disease (NAD) in an outpatient imaging population using comprehensive, objective, and reproducible criteria for NAD. Materials and Methods: The independent validation study includes 15000 patients with chest radiographs in posterior-anterior (PA) and lateral projections from an outpatient imaging center in the United States. Ground truth was established by reviewing CXR reports and classifying cases as NAD or actionable disease (AD). The NAD definition includes completely normal chest radiographs and radiographs with well-defined non-actionable findings. The AI NAD Analyzer 1  (trained with 100 million multimodal images and fine-tuned on 1.3 million radiographs) utilizes a tandem system with image-level rule in and compartment-level rule out to provide case level output as NAD or potential actionable disease (PAD). Results: A total of 14057 cases met our eligibility criteria (age 56 ± 16.1 years, 55% women and 45% men). The prevalence of NAD cases in the study population was 70.7%. The AI NAD Analyzer correctly classified NAD cases with a sensitivity of 29.1% and a yield of 20.6%. The specificity was 98.9% which corresponds to a miss rate of 0.3% of cases. Significant findings were missed in 0.06% of cases, while no cases with critical findings were missed by AI. Conclusion: In an outpatient population, AI can identify 20% of chest radiographs as NAD with a very low rate of missed findings. These cases could potentially be read using a streamlined protocol, thus improving efficiency and consequently reducing daily workload for radiologists. Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Warren B. Gefter reports financial support was provided by Siemens Medical Solutions USA Inc Princeton. Steven L. Mendelson reports financial support was provided by Siemens Medical Solutions USA Inc Princeton. James Sutcliffe reports financial support was provided by Siemens Medical Solutions USA Inc Princeton. Awais Mansoor has patent pending to Siemens Medical Solutions USA. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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