The Role of DICOM in Artificial Intelligence for Skin Disease

Abstract

There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease. At present, AI for skin disease is embedded in research and development and not practiced widely in clinical dermatology. Clinical dermatology is also undergoing a technological transformation in terms of the development and adoption of standards that optimizes the quality use of imaging. Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging. DICOM is a continually evolving standard. There is considerable effort being invested in developing dermatology-specific extensions to the DICOM standard. The ability to encode relevant metadata and afford interoperability with the digital health ecosystem (e.g., image repositories, electronic medical records) has driven the initial impetus in the adoption of DICOM for dermatology. DICOM has a dedicated working group whose role is to develop a mechanism to support AI workflows and encode AI artifacts. DICOM can improve AI workflows by encoding derived objects (e.g., secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation. This can be achieved using DICOM mechanisms such as standardized image formats and metadata, metadata-based image retrieval, and de-identification protocols. DICOM can address several important technological and workflow challenges for the implementation of AI. However, many other technological, ethical, regulatory, medicolegal, and workforce barriers will need to be addressed before DICOM and AI can be used effectively in dermatology.

Artificial Intelligence in Dermatology: Challenges and Perspectives

“…Images are being collected by non-standardized methods via smartphones and cameras without the capacity to include inclusion supplementary material. Caffery et al [ 75 ] gave a detailed explanation of the role of DICOM in AI in dermatology. They highlighted that objects such as resized or down-sampled images, segmentation images, and the algorithm’s lesion classification output, as well as metadata can be attached to a DICOM file [ 75 ].…”

“…Caffery et al [ 75 ] gave a detailed explanation of the role of DICOM in AI in dermatology. They highlighted that objects such as resized or down-sampled images, segmentation images, and the algorithm’s lesion classification output, as well as metadata can be attached to a DICOM file [ 75 ]. The existence of such datasets can eliminate the aforementioned pitfalls in AI.…”

The Future of Precision Prevention for Advanced Melanoma

“…The adoption of standards for dermatology imaging can improve AI workflows by encoding derived objects (e.g., secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation ( 70 ). The use of DICOM for the management of dermatological images will not guarantee effective clinical translation of AI in dermatology but may address important technological and implementation challenges ( 70 ).…”

Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology

“…While DICOM has been primarily designed for medical imaging in clinical practice and includes established mechanisms for ensuring high image quality required for diagnostic purposes, the standard also has several advantages for biomedical imaging research 17 , 25 , 26 . Notably, DICOM specifies profiles for data de-identification that facilitate the use of clinically acquired imaging data sets for research purposes 31 , 32 and defines digital objects for encoding and communicating image annotations and analysis results 17 , 22 , 26 , 27 . Furthermore, there is an abundance of open-source software libraries and tools that support DICOM 33 .…”

“…DICOM is often considered a radiology standard, but it has been widely adopted across medical domains including dermatology, ophthalmology, and endoscopy for a variety of imaging modalities and finds broad application in preclinical and clinical research [20][21][22][23] . In recent years, the standard has been further developed to support slide microscopy 24,25 , quantitative imaging 17,22 , and machine learning 26,27 , and is being adopted internationally for storage, management, and exchange of slide microscopy in diagnostic pathology 28 as well as in research and development 29,30 . While DICOM has been primarily designed for medical imaging in clinical practice and includes established mechanisms for ensuring high image quality required for diagnostic purposes, the standard also has several advantages for biomedical imaging research 17,25,26 .…”