SimpleMind: An open-source software environment that adds thinking to deep neural networks.

Deep neural networks (DNNs) detect patterns in data and have shown versatility and strong performance in many computer vision applications. However, DNNs alone are susceptible to obvious mistakes that violate simple, common sense concepts and are limited in their ability to use explicit knowledge to guide their search and decision making. While overall DNN performance metrics may be good, these obvious errors, coupled with a lack of explainability, have prevented widespread adoption for crucial tasks such as medical image analysis. The purpose of this paper is to introduce SimpleMind, an open-source software environment for Cognitive AI focused on medical image understanding. It allows creation of a knowledge base that describes expected characteristics and relationships between image objects in an intuitive human-readable form. The knowledge base can then be applied to an input image to recognize and understand its content. SimpleMind brings thinking to DNNs by: (1) providing methods for reasoning with the knowledge base about image content, such as spatial inferencing and conditional reasoning to check DNN outputs; (2) applying process knowledge, in the form of general-purpose software agents, that are dynamically chained together to accomplish image preprocessing, DNN prediction, and result post-processing, and (3) performing automatic co-optimization of all knowledge base parameters to adapt agents to specific problems. SimpleMind enables reasoning on multiple detected objects to ensure consistency, providing cross-checking between DNN outputs. This machine reasoning improves the reliability and trustworthiness of DNNs through an interpretable model and explainable decisions. Proof-of-principle example applications are provided that demonstrate how SimpleMind supports and improves deep neural networks by embedding them within a Cognitive AI environment.

Integration of Chest CT CAD into the Clinical Workflow and Impact on Radiologist Efficiency.

RATIONALE AND OBJECTIVES: The purpose of this paper is to describe the integration of a commercial chest CT computer-aided detection (CAD) system into the clinical radiology reporting workflow and perform an initial investigation of its impact on radiologist efficiency. It seeks to complement research into CAD sensitivity and specificity of stand-alone systems, by focusing on report generation time when the CAD is integrated into the clinical workflow. MATERIALS AND METHODS: A commercial chest CT CAD software that provides automated detection and measurement of lung nodules, ascending and descending aorta, and pleural effusion was integrated with a commercial radiology report dictation application. The CAD system automatically prepopulated a radiology report template, thus offering the potential for increased efficiency. The integrated system was evaluated using 40 scans from a publicly available lung nodule database. Each scan was read using two methods: (1) without CAD analytics, i.e., manually populated report with measurements using electronic calipers, and (2) with CAD analytics to prepopulate the report for reader review and editing. Three radiologists participated as readers in this study. RESULTS: CAD assistance reduced reading times by 7%-44%, relative to the conventional manual method, for the three radiologists from opening of the case to signing of the final report. CONCLUSION: This study provides an investigation of the impact of CAD and measurement on chest CTs within a clinical reporting workflow. Prepopulation of a report with automated nodule and aorta measurements yielded substantial time savings relative to manual measurement and entry.