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Jonathan G. Goldin

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NeSy Conference 2023 Conference Paper

Implementing Trustworthy AI in Real-world Medical Imaging using the SimpleMind Software Environment

  • Matthew S. Brown
  • M. Wasil Wahi-Anwar
  • Youngwon Choi
  • Morgan Daly
  • Liza Shrestha
  • Koon-Pong Wong
  • Jonathan G. Goldin
  • Dieter R. Enzmann

Deep neural networks (DNNs) have good overall performance in medical imaging, but they are susceptible to obvious mistakes that violate common sense concepts. Unexplained errors have reduced trust and prevented widespread adoption in real-world clinical practice. We introduce SimpleMind, an open-source Cognitive AI software environment for medical image understanding. It uses a hybrid Neurosymbolic AI approach that integrates both DNNs and machine reasoning from a knowledge base. We demonstrate its use in building trustworthy AI for checking endotracheal tube (ETT) placement on chest X-rays (CXRs). The AI was integrated into clinical practice and the correctness of the ETT misplacement alerts were compared with radiology reports as the reference. 214 CXRs were ordered by ICU physicians to check ETT placement with AI assistance. ETT alert messages had a positive predictive value (PPV) of 42% and a negative predictive value (NPV) of 98%. Physicians indicated that they agreed with the AI outputs, had increased confidence in their decisions, and were more effective with AI assistance.

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AAAI Conference 2020 Short Paper

Domain Knowledge-Assisted Automatic Diagnosis of Idiopathic Pulmonary Fibrosis (IPF) Using High Resolution Computed Tomography (HRCT) (Student Abstract)

  • Wenxi Yu
  • Hua Zhou
  • Jonathan G. Goldin
  • Grace Hyun J. Kim

Domain knowledge acquired from pilot studies is important for medical diagnosis. This paper leverages the populationlevel domain knowledge based on the D-optimal design criterion to judiciously select CT slices that are meaningful for the disease diagnosis task. As an illustrative example, the diagnosis of idiopathic pulmonary fibrosis (IPF) among interstitial lung disease (ILD) patients is used for this work. IPF diagnosis is complicated and is subject to inter-observer variability. We aim to construct a time/memory-efficient IPF diagnosis model using high resolution computed tomography (HRCT) with domain knowledge-assisted data dimension reduction methods. Four two-dimensional convolutional neural network (2D-CNN) architectures (MobileNet, VGG16, ResNet, and DenseNet) are implemented for an automatic diagnosis of IPF among ILD patients. Axial lung CT images are acquired from five multi-center clinical trials, which sum up to 330 IPF patients and 650 non-IPF ILD patients. Model performance is evaluated using five-fold cross-validation. Depending on the model setup, MobileNet achieved satisfactory results with overall sensitivity, specificity, and accuracy greater than 90%. Further evaluation of independent datasets is underway. Based on our knowledge, this is the first work that (1) uses population-level domain knowledge with optimal design criterion in selecting CT slices and (2) focuses on patient-level IPF diagnosis.

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