On-demand teleradiology using smartphone photographs as proxies for DICOM images
This provides a practical alternative for teleradiology, avoiding proprietary and security barriers in DICOM communication, though it is incremental as it builds on existing autoencoder and AI methods.
The paper tackled the problem of transferring medical images between sites by using smartphone photographs as proxies for DICOM images, showing that AI performance with photographs is statistically equivalent to using original DICOM images, with an autoencoder preprocessor increasing PSNR by 15 dB or greater.
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.