DP advances and barriers
WSI serves as the foundational data platform, and is an absolute requirement for the application of AI in AP. Simply put, without digital pathology, there is no artificial intelligence in anatomic pathology (i.e. No DP = No AI). Fortunately, multiple recent advances in WSI have, in theory, paved the way for increased DP adoption by AP laboratories, such as (1) high-throughput scanning (e.g. output of 80–100 slides per hour per WSI device); (2) regulatory approvals for primary clinical use; (3) improved interoperability through imaging standards; and (4) increased pathologist acceptance of using virtual slides [8–10].
The reality surrounding DP adoption, however, is much more complex. WSI, in many ways, has a more complicated value proposition than other clinical imaging modalities (e.g. radiology) owing to its continued dependence on physical workflows (WSI still requires glass slides), substantial storage requirements (100s of TBs to PBs of annual data), diversity of primary and ancillary stains, use of multiple focal planes, reliance on pathologists as part of the clinical validation pathway, and a lack of non-pathology clinician awareness/acceptance. Further, even for simple implementations, the five-year total cost of ownership for DP can be estimated in the millions of dollars when all components of a WSI system are taken into account (WSI devices, image management system, storage, workstations and displays). Although some have shown positive return-on investment for DP implementation efforts [10–12], these studies for the most part are based on potential savings/projections and not on full accounting cost savings. Therefore, in order to see more widespread WSI adoption, a more tangible way of adding value through DP must be found.
AI is the key driver for DP adoption
AI has the means to add significant value to AP by making the AP lab more efficient, automating repetitive tasks for pathologists, elevating case reimbursement and improving patient care. As Figure 2 demonstrates, there are many ways in which AI can be used at all stages of the pathology diagnostic process, including before, during and after the pathologist receives a patient’s whole slide images. Each of these optimizations can be correlated with time savings, cost savings, increased reimbursement and/or improved quality/patient safety (e.g. through measurable key performance indicators).
To realize this added value, however, it is not enough to simply scan one’s glass slides; instead, full interoperability and standardization of WSI is key. Currently, most WSI image management systems (IMS) in use clinically have the capability to interface with the AP laboratory information system in order to correctly identify patients/slides, collate virtual slides into cases and present the slides via an integrated viewer. Depending on the vendor, the IMS may also have the ability to integrate directly with specific AI platforms or to run specific AI algorithms.
Over the past 20 years, DP vendors have made great strides towards standardization and interoperability of WSI formats by conforming their devices and image outputs to the Digital Imaging and Communications in Medicine (DICOM) standard . Although DICOM for WSI is still a work in progress, recent connectathons at national conferences have demonstrated the promise of true WSI interoperability . Beyond DICOM, WSI format openness is also crucial for driving AP’s adoption of AI. Currently, the large number of competing WSI formats poses challenges for pathology AI algorithm development, with some ML techniques poorly generalizable across different image formats . Fortunately, virtually all vendors have now made their image format technical documentation available publicly online.