Dr. ARVIND NERAL
The role of AI in laboratory medicine is rapidly expanding owing to recognition of its potential to improve detection, laboratory workflows, decision support and reduce costs and increase efficiency. Increased healthcare demand has placed pressure on laboratory medicine to improve turnover and optimise efficiency using digitalisation, automation and artificial intelligence (AI). This will bring new challenges for the clinical laboratory. Laboratorians need to understand the utility of AI, its limitations and implementations. Al uses complex algorithms and data from medical and laboratory data to mimic human analysis and this requires accurate and reliable data. As in other domains, artificial intelligence is becoming increasingly important in Pathology . In particular, deep learning –based pattern recognition methods can advance the field of pathology by incorporating clinical, radiologic, and genomic data to accurately diagnose diseases and predict patient prognoses.
One area where digitalisation has progressed rapidly is in whole slide imaging. This allows slides to be transferred between locations. This has also progressed towards the use of AI for interpretation of digital slides. The increased adoption of digital pathology allows the use of AI algorithms to facilitate automatic triaging and quality control along with assisted reading of whole slide images. Provision of high-quality and cost-effective digital histopathology will be beneficial to underserviced, remote areas and whole slide imaging has also found use in distant teaching.
Microscopic morphology remains the gold standard in diagnostic pathology, but the main limitation to morphologic diagnosis is diagnostic variability in bearing error among pathologists. The Gleason grading system is one of the most important prognostic factors in prostate cancer. However, significant interobserver variability has been reported when pathologists have used this system. In order to get a consistent and possibly more accurate diagnosis, it is natural to introduce algorithmic intelligence in the pathology domain, at least in the morphological analysis of tissues and cells. With the help of digital pathology equipment varying from microscopic cameras to whole slide imaging scanners, morphology-based automated pathologic diagnosis has become a reality.
Digital pathology images used in AI are mostly scanned from H&E stained slides. Pathology specimens undergo multiple processes, including formalin fixation, grossing, paraffin embedding, tissue sectioning and staining. Each step of the process and the different devices and software used with the digital imaging scanners can affect aspects of the quality of the digital images, such as color, brightness, contrast and scale. For the best results, it is strongly recommended to alleviate the effect of these variations before using the images in automated analysis work. Normalization is one of the techniques used to reduce such variations.
It is encouraging that the accuracy of automated morphological analyses has improved due to DL technology. The pathologic field in Al is expanding to disease severity assessment and prognosis prediction. Although most Al research in pathology is still focused on cancer detection and grading of tumors, pathological diagnosis is not simply a morphological diagnosis, but is a complex process of evaluation and judgment of various types of clinical data that deal with various organs and diseases. Eventually, there will be a medical AI of the prognostic prediction model. Also, a new grading system applicable to several tumors can be created by an AI model that has learned from the patient’s prognosis combined with a number of other variables including morphology, treatment modality, tumor markers, etc. This will also help to overcome the poor reproducibility, leading to much better clinical outcomes for patients.
(Writer is Professor & H.O.D. Department of Pathology Pt. J.N.M. Medical College Raipur ( C.G.). Views are personal).
