ArticleContemporary clinical trials communications2019
Statistical considerations for testing an AI algorithm used for prescreening lung CT images.
Article in Contemporary clinical trials communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 27 citations in OpenAlex.
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- Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study.Insights into imaging · 2022Article
- Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts.Journal of digital imaging · 2022Article
- Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification.Cancers · 2022Review
- Article
- Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use.Journal of digital imaging · 2021Article
- Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?Translational lung cancer research · 2021Review
- Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?Medical oncology (Northwood, London, England) · 2021Review
- Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.Korean journal of radiology · 2020Review
Corrections and comments
- Erratum issued
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence, as applied to medical images to detect, rule out, diagnose, and stage disease, has seen enormous growth over the last few years. There are multiple use cases of AI algorithms in medical imaging: first-reader (or concurrent) mode, second-reader mode, triage mode, and more recently prescreening mode as when an AI algorithm is applied to the worklist of images to identify obvious negative cases so that human readers do not need to review them and can focus on interpreting the remaining cases. In this paper we describe the statistical considerations for designing a study to test a new AI prescreening algorithm for identifying normal lung cancer screening CTs. We contrast agreement vs. accuracy studies, and retrospective vs. prospective designs. We evaluate various test performance metrics with respect to their sensitivity to changes in the AI algorithm's performance, as well as to shifts in reader behavior to a revised worklist. We consider sample size requirements for testing the AI prescreening algorithm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.