Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review
Nagi Ravleen, Aravinda Konidena, Rakesh N., Gupta Rajesh, Pal Ajay, Mann Amrit Kaur,
소속 상세정보
( Nagi Ravleen ) - Swami Devi Dayal Hospital and Dental College Department of Oral Medicine and Radiology
( Aravinda Konidena ) - Swami Devi Dayal Hospital and Dental College Department of Oral Medicine and Radiology
( Rakesh N. ) - M.S. Ramaiah University of Applied Sciences Faculty of Dental Sciences Department of Oral Medicine and Radiology
( Gupta Rajesh ) - Swami Devi Dayal Hospital and Dental College Department of Oral Medicine and Radiology
( Pal Ajay ) - Swami Devi Dayal Hospital and Dental College Department of Oral Medicine and Radiology
( Mann Amrit Kaur ) - Swami Devi Dayal Hospital and Dental College Department of Oral Medicine and Radiology
Abstract
Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.
키워드
Artificial Intelligence; Deep Learning; Machine Learning; Radiology
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