The role of artificial intelligence in veterinary radiation oncology. (13th December 2022)

• Record Type: Journal Article
• Authors: Leary, Del; Basran, Parminder S.
• Is Part Of: Veterinary radiology & ultrasound. Volume 63(2022)supplement 1
• Page Start: 903
• Page End: 912
• Publication Date: 2022-12-13
• Subjects: adaptive radiotherapy -- artificial intelligence -- deep learning -- machine learning -- radiotherapy; Veterinary radiology -- Periodicals; Radiologie vétérinaire -- Périodiques; Échographie vétérinaire -- Périodiques; Radiography -- Periodicals; Ultrasonography -- Periodicals; Diagnosis -- Periodicals; Veterinary Medicine -- Periodicals; Societies, Medical -- Periodicals; 636.089607
• DOI: 10.1111/vru.13162
• Languages: English
• ISSNs: 1058-8183

Abstract

Abstract: Veterinary radiation oncology regularly deploys sophisticated contouring, image registration, and treatment planning optimization software for patient care. Over the past decade, advances in computing power and the rapid development of neural networks, open‐source software packages, and data science have been realized and resulted in new research and clinical applications of artificial intelligent (AI) systems in radiation oncology. These technologies differ from conventional software in their level of complexity and ability to learn from representative and local data. We provide clinical and research application examples of AI in human radiation oncology and their potential applications in veterinary medicine throughout the patient's care‐path: from treatment simulation, deformable registration, auto‐segmentation, automated treatment planning and plan selection, quality assurance, adaptive radiotherapy, and outcomes modeling. These technologies have the potential to offer significant time and cost savings in the veterinary setting; however, since the range of usefulness of these technologies have not been well studied nor understood, care must be taken if adopting AI technologies in clinical practice. Over the next several years, some practical and realizable applications of AI in veterinary radiation oncology include automated segmentation of normal tissues and tumor volumes, deformable registration, multi‐criteria plan optimization, and adaptive radiotherapy. Keys in achieving success in adopting AI in veterinary radiation oncology include: establishing "truth‐data"; data harmonization; multi‐institutional data and collaborations; standardized dose reporting and taxonomy; adopting an open access philosophy, data collection and curation; open‐source algorithm development; and transparent and platform‐independent code development.