| Literature DB >> 34341753 |
Jimena Olveres1,2, Germán González2, Fabian Torres1,2, José Carlos Moreno-Tagle2, Erik Carbajal-Degante3, Alejandro Valencia-Rodríguez4, Nahum Méndez-Sánchez4,5, Boris Escalante-Ramírez1,2.
Abstract
Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.Entities:
Keywords: Artificial intelligence (AI); cardiology; computer vision (CV); gastroenterology; medical image analysis; microscopy; neurodegenerative disorders; oncology; respiratory diseases
Year: 2021 PMID: 34341753 PMCID: PMC8245941 DOI: 10.21037/qims-20-1151
Source DB: PubMed Journal: Quant Imaging Med Surg ISSN: 2223-4306