Noninterpretive Uses of Artificial Intelligence in Radiology

Richardson, Michael L.; Garwood, Elisabeth R.; Lee, Yueh Z.; Li, Matthew; Lo, Hao S.; Nagaraju, Arun; Nguyen, Xuan V.; Probyn, Linda; Rajiah, Prabhakar; Sin, Jessica M.; Wasnik, Ashish P.; Xu, Kali

doi:10.1016/j.acra.2020.01.012

Cited by 70 publications

(57 citation statements)

References 90 publications

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“…A number of resources have emerged attempting to specifically address this concern for radiology professionals taught to varying levels of ability. 10 , 11 , 12 , 13 , 14 , 15 , 16 While these resources are tailored to imaging applications, as of writing we have found few examples of formal integration into residency training.…”

Section: Introductionmentioning

confidence: 99%

AI-RADS: An Artificial Intelligence Curriculum for Residents

et al. 2021

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Rationale and Objectives: Artificial intelligence (AI) has rapidly emerged as a field poised to affect nearly every aspect of medicine, especially radiology. A PubMed search for the terms "artificial intelligence radiology" demonstrates an exponential increase in publications on this topic in recent years. Despite these impending changes, medical education designed for future radiologists have only recently begun. We present our institution's efforts to address this problem as a model for a successful introductory curriculum into artificial intelligence in radiology titled AI-RADS. Materials and Methods: The course was based on a sequence of foundational algorithms in AI; these algorithms were presented as logical extensions of each other and were introduced as familiar examples (spam filters, movie recommendations, etc.). Since most trainees enter residency without computational backgrounds, secondary lessons, such as pixel mathematics, were integrated in this progression. Didactic sessions were reinforced with a concurrent journal club highlighting the algorithm discussed in the previous lecture. To circumvent often intimidating technical descriptions, study guides for these papers were produced. Questionnaires were administered before and after each lecture to assess confidence in the material. Surveys were also submitted at each journal club assessing learner preparedness and appropriateness of the article. Results: The course received a 9.8/10 rating from residents for overall satisfaction. With the exception of the final lecture, there were significant increases in learner confidence in reading journal articles on AI after each lecture. Residents demonstrated significant increases in perceived understanding of foundational concepts in artificial intelligence across all mastery questions for every lecture. Conclusion: The success of our institution's pilot AI-RADS course demonstrates a workable model of including AI in resident education.

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Section: Introductionmentioning

confidence: 99%

AI-RADS: An Artificial Intelligence Curriculum for Residents

et al. 2021

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“…In recent years, deep learning (DL) has been introduced into computer-aided diagnosis (CAD) systems to improve the accuracy of medical imaging diagnosis, save time, and explore new directions and opportunities in radiology. 6 , 7 Applying CAD systems may not only reduce radiologists’ workload but also lessen subjective and ambiguous reporting. Many CAD studies on thyroid imaging have been performed, 8 – 12 including the application of CAD to ultrasound images for the discrimination of benign and malignant thyroid nodules 8 , 9 and CT-based CAD 10 , 11 for the detection of thyroid abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for intelligent diagnosis in thyroid scintigraphy

et al. 2021

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Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

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“…Several AI algorithms have been proposed to resolve a large range of pre-processing problems, including noise reduction, reduction of contrast dose and radiation, and quality control [38,39] These models can be applied not only in atherosclerotic plaque analysis but in the whole of CT imaging.…”

Section: Ai In Pre-acquisition and Acquisitionmentioning

confidence: 99%

Artificial intelligence in computed tomography plaque characterization: A review

Cau

Flanders

Mannelli

et al. 2021

European Journal of Radiology

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Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD.Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice.In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.

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Noninterpretive Uses of Artificial Intelligence in Radiology

Cited by 70 publications

References 90 publications

AI-RADS: An Artificial Intelligence Curriculum for Residents

AI-RADS: An Artificial Intelligence Curriculum for Residents

Deep learning for intelligent diagnosis in thyroid scintigraphy

Artificial intelligence in computed tomography plaque characterization: A review

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