On-demand teleradiology using smartphone photographs as proxies for DICOM images

TL;DR

This paper investigates using smartphone photographs of medical images as proxies for DICOM files, enabling remote analysis and overcoming transfer and security challenges in teleradiology.

Contribution

It introduces an autoencoder preprocessor that enhances photograph quality and demonstrates that AI analysis on photos is statistically equivalent to using original DICOM images.

Findings

Autoencoder improves PSNR by over 15 dB.

AI performance on photographs matches that on DICOM images.

Photograph-based approach offers a secure alternative to traditional teleradiology methods.

Abstract

The use of photographs of the screen of displayed medical images is explored to circumvent the challenges involved in transferring images between sites. The photographs can be conveniently taken with a smartphone and analyzed remotely by either human or AI experts. An autoencoder preprocessor is shown to improve the performance for human experts. The AI performance provided by photographs is shown to be statistically equivalent to using the original DICOM images. The autoencoder preprocessor increases the PSNR by 15 dB or greater and provides an AUC that is statistically equivalent to using the original DICOM images. The photo approach is an alternative to IHE-based teleradiology applications while avoiding the problems inherit in navigating the proprietary and security barriers that limit DICOM communication between PACS in practice.