In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment

• Published in: Journal of biomedical optics Vol. 27; no. 4; p. 040601
• Main Authors: Bishop, Kevin W, Maitland, Kristen C, Rajadhyaksha, Milind, Liu, Jonathan T. C
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.04.2022; S P I E - International Society for
• Subjects: Artificial Intelligence; Best practice; Biopsy; Breast cancer; Cancer therapies; Commercialization; Computational neuroscience; Decision support systems; Dermatology; Diagnosis; Disease; Disease detection; Endoscopy; Forecasting; Health services; Histology; Histopathology; Intravital Microscopy; Medical imaging; Metabolism; Microscopes; Microscopy; Microscopy - methods; Morphology; Neurosurgery; Optimization; Pathology; Patients; Prostate; Skin cancer; Surveillance; Translation; microscopy; commercialization; clinical translation; multimodal imaging; artificial intelligence; in vivo microscopy
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.27.4.040601

Significance: There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology). Aim: Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption. Approach: We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations. Results: While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow. Conclusions: The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.