Abstract
.We present a roadmap for integrating artificial intelligence (AI)-based image analysis algorithms into existing radiology workflows such that (1) radiologists can significantly benefit from enhanced automation in various imaging tasks due to AI, and (2) radiologists’ feedback is utilized to further improve the AI application. This is achieved by establishing three maturity levels where (1) research enables the visualization of AI-based results/annotations by radiologists without generating new patient records; (2) production allows the AI-based system to generate results stored in an institution’s picture-archiving and communication system; and (3) feedback equips radiologists with tools for editing the AI inference results for periodic retraining of the deployed AI systems, thereby allowing continuous organic improvement of AI-based radiology-workflow solutions. A case study (i.e., detection of brain metastases with T1-weighted contrast-enhanced three-dimensional MRI) illustrates the deployment details of a particular AI-based application according to the aforementioned maturity levels. It is shown that the given AI application significantly improves with feedback coming from radiologists; the number of incorrectly detected brain metastases (false positives) decreases from 14.2 to 9.12 per patient with the number of subsequently annotated datasets increasing from 93 to 217 as a result of radiologist adjudication.
Highlights
Artificial intelligence (AI) has been utilized for decades to address a variety of medical imaging problems, such as image segmentation[1], registration[2], detection, and classification[3]
The major components of this investigation, including (1) the algorithmic details (e.g., deep neural networks (DNNs) architecture, data augmentation steps, training methodology, etc.), (2) analysis of statistical properties of the brain metastases (BM) included in the study, and (3) adherence to data-acquisition criteria, have been comprehensively described in a previous report.[22]
The results of this work show that the amount of data is a determining factor for the accuracy of the AI approach used in the case study
Summary
Artificial intelligence (AI) (decision trees, regression algorithms, support vector machines, Bayesian methods, neural networks, etc.) has been utilized for decades to address a variety of medical imaging problems, such as image segmentation[1] (i.e., finding the borders of a target object), registration[2] (i.e., visually aligning anatomical parts in single- or multimodality images), detection (i.e., detecting formations/structures), and classification[3] (i.e., grouping of medical information in subgroups). It can facilitate information feeds in radiology workflows[4,5] (e.g., natural language processing in dictation systems). DL-based solutions are commonly built on vast amounts of data.[7]
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