Johann-Martin Hempel1, Daniel Pinto Dos Santos2. 1. Radiologische Universitätsklinik, Abteilung Diagnostische und Interventionelle Neuroradiologie, Uniklinik Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Deutschland. johann-martin.hempel@uni-tuebingen.de. 2. Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 50937, Köln, Deutschland.
Abstract
BACKGROUND: There are a multitude of application possibilities of artificial intelligence (AI) and structured reporting (SR) in radiology. The number of scientific publications have continuously increased for many years. There is an extensive portfolio of available AI algorithms for, e.g. automatic detection and preselection of pathologic patterns in images or for facilitating the reporting workflows. Even machines already use AI algorithms for improvement of operating comfort. METHOD: The use of SR is essential especially for the extraction of automatically evaluable semantic data from radiology results reports. Regarding eligibility in certification processes, the use of SR is mandatory for the accreditation of the German Cancer Society as an oncological center or outside Germany, such as the European Cancer Center. RESULTS: The data from SR can be automatically evaluated for the purpose of patient care, research and educational purposes and quality assurance. Lack of information and a high degree of variability often hamper the extraction of valid information from free-text reports using neurolinguistic programming (NLP). Against the background of supervised training, AI algorithms or k‑nearest neighbors (KNN) require a considerable amount of validated data. The semantic data from SR can also be processed by AI and used for training. CONCLUSION: The AI and SR are separate entities within the field of radiology with mutual dependencies and significant added value. Both have a high potential for profound upcoming changes and further developments in radiology.
BACKGROUND: There are a multitude of application possibilities of artificial intelligence (AI) and structured reporting (SR) in radiology. The number of scientific publications have continuously increased for many years. There is an extensive portfolio of available AI algorithms for, e.g. automatic detection and preselection of pathologic patterns in images or for facilitating the reporting workflows. Even machines already use AI algorithms for improvement of operating comfort. METHOD: The use of SR is essential especially for the extraction of automatically evaluable semantic data from radiology results reports. Regarding eligibility in certification processes, the use of SR is mandatory for the accreditation of the German Cancer Society as an oncological center or outside Germany, such as the European Cancer Center. RESULTS: The data from SR can be automatically evaluated for the purpose of patient care, research and educational purposes and quality assurance. Lack of information and a high degree of variability often hamper the extraction of valid information from free-text reports using neurolinguistic programming (NLP). Against the background of supervised training, AI algorithms or k‑nearest neighbors (KNN) require a considerable amount of validated data. The semantic data from SR can also be processed by AI and used for training. CONCLUSION: The AI and SR are separate entities within the field of radiology with mutual dependencies and significant added value. Both have a high potential for profound upcoming changes and further developments in radiology.
Authors: Dhakshinamoorthy Ganeshan; Phuong-Anh Thi Duong; Linda Probyn; Leon Lenchik; Tatum A McArthur; Michele Retrouvey; Emily H Ghobadi; Stephane L Desouches; David Pastel; Isaac R Francis Journal: Acad Radiol Date: 2017-10-10 Impact factor: 3.173
Authors: Matthew C Chen; Robyn L Ball; Lingyao Yang; Nathaniel Moradzadeh; Brian E Chapman; David B Larson; Curtis P Langlotz; Timothy J Amrhein; Matthew P Lungren Journal: Radiology Date: 2017-11-13 Impact factor: 11.105