Pathology is the study and diagnosis of the disease through examination of the body tissue, which is fixed on the glass slides under the microscope. Digital Pathology is on the verge of becoming the main option for routine diagnostics. It is the practice of using digital imaging in pathology. Whole Side Imaging technology (WSI) has paved the way for this development. The advent of digitized images had propelled this traditional pathology which is now known as digital pathology. Digital images and video can now be shared in real-time thus bridging the physical distance between the hospitals (Pathology), colleagues, teachers (second opinion), and between home and workplace (work office). Digital images both lend themselves to a computational path (CPATH), for both measuring and counting Machine Learning tasks. The slides can be accessed from anywhere in the world for delivering accurate results in comparison to the traditional method.
Workflow of traditional Pathology and Digital Pathology
Firstly, body tissue is examined in the pathology lab after that glass is couriered by a pathology worker and after that glass slide is analysed and then it is delivered to the diagnosed patients. This process is time-consuming and expensive as slides are moved around from one place to another due to which patient health gets compromised. It is one of the reasons that digital pathology is used now. In digital pathology firstly body is examined in the lab after that digital slides are created and after that slides are analysed by a computer and the report is delivered to the patient. Digital Pathology produces accurate results in less time than traditional takes.
Applications of Digital Pathology
- Personalized Treatments: AI along with Digital pathology and other resources available with health professionals can lead to more patient-centric treatments. It helps health professionals to make more conclusions about the patients.
- Improved Efficiency: AI when applied to digital pathology provides results faster than humans. Furthermore, AI can help in diagnosing a variety of cancer or triaging biopsies.
- Positive Changes to Workflow: Recently developed algorithm of AI Lymph Node Assistant helps health professionals to analyse metastatic breast cancer which is difficult for humans to find so accurate which saves time for health professionals in making reports and patients health is not compromised.
- More Time Devoted to Cases: Many pathology departments lack suffice no. of working professionals and if a vast amount of data is generated it can create a problem. AI helps pathologists to investigate which are normal or abnormal cases.
- Cost-Effective: AI in Digital pathology helps in reducing cost eliminating multiple steps and transportation services.
- Reduced Errors: Digital slides eliminate the risk of breaking the slides, and barcoding leads to the risk of misidentification.
Digital pathology is a key part of the Remote Healthcare industry which is growing at a compounded annual growth rate of ~20% during 2021-2027, as per Astute Analytica. The market research firm predicted that the growth of the remote healthcare industry is majorly driven by the integration of advanced technology with healthcare facilities at a rapid pace. Along with digital pathology, the remote healthcare industry consists of telemedicine, advanced patient monitoring, EHRs and other patient portals for appointments and record tracking.
Limitations of the AI and Digital Pathology
- Data Safety: Data Safety is the most important factor in the process as robust networks are required to process data and to ensure a high level of confidentiality as per the guidelines.
- Data Storage: Large data storage is required to store the data as there are high-resolution images, which serves to be a challenge, along with their transmission through systems.
- High Investments: Significant investments are required to set up the networks for digital pathology. The ambiguity and slow return investment is hampering the growth and adoption of digital pathology.
- Non-Standardization: With no standardization in the digital pathology process there are limited choices available in the market.
Trends in Digital Pathology
Many top clinical research organizations and established biopharmaceutical companies have adopted the concept of digital pathology to increase their drug development process. Digital pathology is experiencing a positive and exponential growth and it will be facilitating advanced diagnostic skills for health professionals. Digital pathology companies or labs need to consider reconstructing the business framework to support the development of medicine using digital pathology.
Recently systems have designed Image Microarrays (IMAs) that allow the mining of digital archives of slides. These image microarrays allow digital slides to be converted into an array of high-resolution images, with each image in the array providing essential diagnostic morphologies.
Several algorithms are being developed such as Computer-assisted Image Analysis (CAIA) has been used to mark certain chemical stains, which gives all pathologists the same yardstick for scoring immunohistochemistry findings in cancer. Additionally, pattern recognition algorithms is an algorithm that improves reliability, specificity, and accuracy.
The increased use of technologies such as Machine Learning, AI in digital pathology, and medicine are expected to grow the remote healthcare market.