The role of artificial intelligence in clinical imaging and workflows

Abstract

Evidence‐based medicine, outcomes management, and multidisciplinary systems are laying the foundation for radiology on the cusp of a new day. Environmental and operational forces coupled with technological advancements are redefining the veterinary radiologist of tomorrow. In the past several years, veterinary image volumes have exploded, and the scale of hardware and software required to support it seems boundless. The most dynamic trend within veterinary radiology is implementing digital information systems such as PACS, RIS, PIMS, and Voice Recognition systems. While the digitization of radiography imaging has significantly improved the workflow of the veterinary radiology assistant and radiologist, tedious, redundant tasks are abundant and mind‐numbing. They can lead to errors with a significant impact on patient care. Today, these boring and repetitious tasks continue to bog down patient throughput and workflow. Artificial intelligence, particularly machine learning, shows much promise to rocket the workflow and veterinary clinical imaging into a new day where the AI management of mundane tasks allows for efficiency so the radiologist can better concentrate on the quality of patient care. In this article, we briefly discuss the major subsets of artificial intelligence (AI) workflow for the radiologist and veterinary radiology assistant including image acquisition, segmentation and mensuration, rotation and hanging protocol, detection and prioritization, monitoring and registration of lesions, implementation of these subsets, and the ethics of utilizing AI in veterinary medicine.

Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation

“…Within veterinary medicine, this research has so far focused on radiology rather than echocardiography. 1,2 Of the 40 peer-reviewed studies since 2015, none used AI to evaluate echocardiographic images. 3,4 AI has potential to contribute automatic assistance in the analysis of echocardiographic images.…”

“…Artificial intelligence (AI) techniques have been successfully applied to automate human‐led diagnostic imaging analysis tasks. Within veterinary medicine, this research has so far focused on radiology rather than echocardiography 1,2 . Of the 40 peer‐reviewed studies since 2015, none used AI to evaluate echocardiographic images 3,4 …”

“…Within veterinary medicine, this research has so far focused on radiology rather than echocardiography. 1 , 2 Of the 40 peer‐reviewed studies since 2015, none used AI to evaluate echocardiographic images. 3 , 4 …”

An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs

“…Additionally, the incidence and causes of radiographic technical errors in veterinary clinical practice are poorly understood 4 . In human medicine, speci c guidelines outlining acceptable diagnostic image quality standards are available 5 . However, to the best of the authors' knowledge, such guidelines do not exist in veterinary medicine.…”

An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs

“…The use of artificial intelligence (AI) in veterinary diagnostic imaging is experiencing a rapid increase in popularity, as more veterinarians become aware of the benefits offered by this technology 7 . This has led to a corresponding rise in the number of published works exploring the various applications of AI in the field of veterinary medicine.…”