Artificial Intelligence and Infectious Disease Imaging

Abstract

The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.

Keywords: AI; artificial intelligence; imaging; infectious disease.

Figure 1.

Self-supervised learning for medical image segmentation. The diagram describes an implementation of how unlabeled computed tomographic (CT) scans and self-supervised learning (specifically contrastive learning) can be used to enhance the performance of a supervised learning segmentation model. First, unlabeled scans are augmented using simple transformations, such as cropping, rotation, and blurring. These augmented scans are inputted into the self-supervised model, and the model is tasked with distinguishing augmented images that come from the same source image from augmented images that come from different images (ie, pretask). After training, the pretrained encoders can be transferred to a supervised learning model, which is given a small batch of labeled scans and tasked with producing the segmentation masks. Pretraining with a self-supervised learning task has been shown to enhance the performance of supervised learning models.

Figure 2.

Explainable artificial intelligence (AI) methods in medical imaging. Standard deep learning models are uninterpretable and therefore work as a “black box.” Explainable AI methods, such as ablation-based, attention-based, and concept-based methods, provide clinicians and researchers with additional information about how the model forms its predictions. Abbreviation: GGO, ground-glass opacities.

Figure 3.

Grad-CAM compared with information bottleneck attribution (IBA) attention maps. Left, Computed tomographic (CT) scan with subtle ground-glass opacities (GGO) pattern. The proposed IBA shows the exact location of pathology without false-positives and precisely, while Grad-CAM fails. Arrows on the first row are pointing to areas within the image that are not true lesions but false positives predicted by Grad-Cam. Arrows on the bottom row point to true lesions not detected by Grad-Cam.