The Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group, consisting of toxicologic pathologists from pharmaceutical companies, contract research organizations, private practice and medical cancer centers published a paper in the upcoming Journal of Toxicologic Pathology titled “Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology”.
The in-depth paper covers many aspects of the use of artificial intelligence in toxicologic pathology, including the utilization of augmented reality in the microscope – citing Augmentiqs.
“Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues.”
The Opinion Paper cites the Augmented Reality microscope for its ability to display annotations, measurements and other digital information on top of the optical field of view as seen within the microscope eyepiece.
“Augmented reality uses various instruments ranging from seethrough visors to smartphone screens, but in the toxicologic pathologist’s case, a microscope, to layer computer generated,
virtual objects over the viewer’s actual, physical surroundings. Augmented reality already assists clinicians during delicate surgeries by creating an overlay of anatomical structures, for example, those reconstructed from the patient’s computed tomographic (CT) data, that can be referenced by the surgeon as a guide during the procedure.Similar technology is being used to train medical students and practitioners. The range of AR applications has grown to include some that are useful in pathology. Like in surgery, AI can aid performing autopsies, by creating an overlay of CT data.It can also aid the training of pathology residents to perform autopsies, by instructing them with real-time diagrams, annotations, and voice instructions.Augmented reality can also be used to annotate areas of interest on surgical specimens in real-time, and improve identifying the precise anatomical location of important pathologic findings by overlaying previously obtained radiographs onto the samples.
Furthermore, AR can be used to annotate photographs taken during gross examinations, to facilitate communication between pathology technicians and pathologists.
Augmented reality may also improve the accuracy and efficiency of the pathologist, as in the case of microscope-integrated telepathology, which allows real-time sharing of the precise field of view being seen by one individual with other experts. The technology also allows the projection of an augmented overlay of digital information, such as measurements, annotations, and information regarding the study, on top of the optical field of view.
Deep learning algorithms have also been developed in the AR space that has been used to assist the pathologist in detecting breast cancer metastases in lymph nodes and in classifying prostate cancer according to the Gleason scoring system.These tools enable real-time image analysis highlighting the areas of interest directly into Turner et al. 11 the field of view allowing for the potential of real-time assessment by pathologists and ML algorithms.”
Click here to read the paper on Pubmed.