1. AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
- Author
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Spaanderman, Douwe J., Marzetti, Matthew, Wan, Xinyi, Scarsbrook, Andrew F., Robinson, Philip, Oei, Edwin H. G., Visser, Jacob J., Hemke, Robert, van Langevelde, Kirsten, Hanff, David F., van Leenders, Geert J. L. H., Verhoef, Cornelis, Gruühagen, Dirk J., Niessen, Wiro J., Klein, Stefan, and Starmans, Martijn P. A.
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9$\pm$7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1$\pm$2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods., Comment: 23 pages, 6 figures, 6 supplementary figures
- Published
- 2024