Portal de Periódicos da CAPES

Utilization of deep learning techniques to assist clinicians in diagnostic and interventional radiology: Development of a virtual radiology assistant

2017; Elsevier BV; Volume: 28; Issue: 2 Linguagem: Inglês

10.1016/j.jvir.2016.12.974

1535-7732

Kevin Seals, Brian L Dubin, Laura Leonards, E Lee, Justin P. McWilliams, Stephen T. Kee, Robert D. Suh,

Radiology practices and education

Improved artificial intelligence through deep learning has the potential to fundamentally transform our society, from automated image analysis to the creation of self-driving cars. We apply these techniques to create a virtual radiology assistant that offers clinicians many non-interpretive radiology skills. A wide range of functionality is offered, from helping select the optimal imaging study or procedure for a given clinical scenario to describing the follow-up of incidental findings, guiding contrast administration in renal failure/contrast allergy, and describing optimal peri-procedural patient management. The application was built in Xcode using the Swift programming language. The user interface consists of text boxes arranged in a manner simulating communication via traditional SMS text messaging services. Natural Language Processing (NLP) was implemented using the Watson Natural Language Classifier application program interface (API). Using this classifier, user inputs are understood and paired with relevant information categories of interest to clinicians. For example, if a clinician asks whether IVC filter placement is appropriate for a particular patient, they will be paired with an IVC filter category and relevant information will be provided. This information can come in multiple forms, including relevant websites, infographics, and subprograms within the application. Model performance improves significantly as more training data is provided. Successful input categorization is performed in most cases, particularly for focused queries providing sufficient detail. Using a confidence probability threshold of 0.7, the natural language classifier provides optimal information categorization with minimal inclusion of extraneous categories. Deep learning techniques can be used to create powerful artificial intelligence tools to assist clinicians. These tools both allow clinicians to rapidly access useful information and reduce the need for radiologists to perform redundant communication tasks that potentially distract from patient care.