Exploring the Latest Developments in Digital Pathology
Tech startups are exploring the use of AI in the pathology field, and deep learning is being used to address a wide range of problems in pathology, such as detecting sclerotic glomeruli in the kidney and quantifying liver fibrosis. As AI becomes more widely accepted in the pathologists’ community, there is a paradigm shift in the digital pathology industry towards the use of deep learning, with companies moving away from traditional image analysis methods and instead offering deep learning as a customized solution or as a software-as-a-service (Saas).
Additionally, companies are now developing decision support systems where pathologists have the final say on the software’s results. Finally, digital pathology and image analysis software is becoming more user-friendly.
Adoption of Deep Learning in Digital Pathology and Image Analysis
The trend of deep learning in the field of image analysis is now an integral part of digital pathology and tissue image analysis projects. Traditional image analysis software can be difficult for non-programmers to use, and that manual selection of features can be limiting in heterogeneous data sets. As a result, companies are turning to deep learning and artificial neural networks to improve the performance and user-friendliness of their image analysis systems. Companies like Aiforia are creating systems based solely on deep learning, while “veterans” in the digital pathology market, like Visiopharm and Indica Labs, are adding AI modules to stay competitive. AI-based image analysis systems allow users to give the system examples without needing to precisely define detection criteria, and performance can be reviewed and improved with more examples. Some programs, like the Studio by DeePathology, even guide the user in generating examples. Additionally, many newer market players are using open application programming interfaces (open APIs), making it easier to integrate user-generated solutions into other computerized systems like lab information management systems.
Transitioning to Cloud-Based Systems in Digital Pathology and Image Analysis
One of the main challenges with deep learning is the need for powerful computers to process images, which can be costly. In response, companies have started moving their services into the cloud, which allows users to access the service through an internet browser and eliminates the need for expensive equipment. Additionally, cloud-based systems are easier to integrate into large organizations, as they do not require integration into the IT infrastructure. However, there are also security concerns with remote access and the possibility of competitors using the same cloud-based systems. These concerns must be addressed before starting a collaboration to ensure that both parties are confident moving forward.
PATHOLOGIST’S DECISION SUPPORT SYSTEMS
The recent acceptance of deep learning as an integral part of digital pathology is associated with the shift from standalone solutions toward pathology decision support systems.
As pathologists, we are responsible for every case we diagnose and every slide in the study we evaluate. Letting the computer analyze something for us and not being able to influence it frightens us, and rightly so. There are very few algorithms or digital solutions for pathology, which are mature enough not to need a pathologist’s supervision. The lack of AI FDA-approved pathology tools reflects that. Digital pathology companies have slowly begun to understand this and adapt to the needs of pathologists. The new trend is to show pathologists the results of the computer software and let them decide whether it is good enough or not. This is much more adapted to the current pathologist workflow, where they must review every slide anyway. Highlighting suspicious areas can significantly accelerate the review process, adding great value.
However, the acceleration of the process cannot compromise the quality of the sample assessment in any way. Faster also needs to mean better, not worse. Only in this case will any technological advancement be justifiable. Regardless of the tools provided, the pathologist is still ultimately responsible for the sign-out, and they need to stay as professional and thorough as they were when they were working without computer assistance. They need to have the same level of confidence in the “accelerated” sign-out as they have in the classical, manual one. They are still responsible for finding the hidden corner of the slide containing a micro-metastasis, even if it was not detected by the algorithm. This remains the pathologist’s responsibility, and only if it stays that way, will pathologists get on board and become more open to experimenting with new methods and technologies targeting their efficiency.
As in the Pap smear review example, where the cytotechnologist screens the samples and the suspicious cases go back to the pathologist, a percentage of the negative cases also needs to be checked for quality assurance. In the Pap smear case, 10% of the negative cases should be evaluated by the pathologist. How much will it be when instead of a cytotechnologist AI will be our assistant? Nobody knows yet as there are no AI-based pathology solutions that have undergone a scrutinous review by the FDA. We will have to figure it out. Will it be 10% or 50% or will we need to review all negative cases? This probably depends on the robustness of the AI model we will be using. Would a necessity to review 100% of negative cases still accelerate our work? This probably depends on how much faster we will be evaluating the positive cases with AI assistance.
Currently, toxicopathologists and scientists responsible for drug development are looking into unsupervised deep-learning methods to differentiate normal tissues from abnormal tissues and highlight the “abnormalities”. The detected abnormalities may be anything the system did not learn from the normal control group, but the decisions about their relevance are left to the pathologist. As toxicopathological studies contain hundreds (and sometimes thousands) of slides, this may have the potential to significantly accelerate the drug development process.
Will AI deliver on this promise? Time, reality, and regulatory authorities will verify. It is high time to start working closely with the authorities regulating the drug development process. It is possible to use digital pathology and image analysis for Practice-compliant studies, but someone needs to take the first step and perform extensive validation. The first one who does that will pave the way for others and will immensely advance toxicologic pathology. It will be a great effort, but so was the Philipps IntelLiSite validation, where the company recognized the enormous value of this process and successfully paved the way for others.
USER-FRIENDLY
Another paradigm shift (occurring somewhat late, given that pathologist is supposed to be end-users of the digital pathology software) is the shift towards better usability. The software is becoming more user-friendly.
For a long time, digital pathology software looked like it was designed by software programmers for other programmers, maybe less advanced ones, but programmers. This is still visible in some software packages where instead of clicking “yes” or “no” you still must type the truth value “1” or “0”.
Truth value? As an answer to a yes or no question? This is not pathologist friendly!
The user interface is changing both for IA software (especially the newer players on the market like Aiforia have it as a priority) as well as for slide viewers and slide management systems. The workflows for clinical and diagnostic pathology and toxicologic pathology supporting the preclinical phase of drug development differ significantly. For a long time, toxicopathologists had to work with slide viewers optimized for diagnostic pathology, which made them angry and discouraged them from digital pathology. However, they didn’t have a choice as there was nothing else available on the market. Now there are companies like Deciphex focusing only on toxicopathologists and their slide viewer Patholytix preclinical is optimized for the preclinical pathology workflow.
Additionally, image analysis solution providers recognized the plethora of pathology problems that can be addressed with AI, but rather than designing hard-coded solutions and later selling them to pathologists who may have completely different problems, they made the deep learning methods accessible to pathologists as customizable tool for solving their particular problems. Pathologists and researchers can now train and correct the models on demand and once a robust algorithm is built and validated, it could be integrated into their daily workflow, like the H. Pylori Decision Support System designed by DeePathology with their software The Studio.
DIGITAL PATHOLOGY INSIDE OF THE MICROSCOPE
The digital pathology providers even started providing solutions (hardware and software) for those who want to keep working with the microscope. The company Augmentiqs provides an alternative, flat camera unit (aka augmented reality module) which can be attached to your microscope between the objectives and the eyepiece to enable working with digital and analog pathology at the same time. The image from the microscope is transmitted live to the computer where not only can it be shared live with collaborators, but also digital image analysis algorithms and deep learning models can be applied. This gives it a great advantage over a c-mounted microscope camera for capturing static images, where the live-sharing experience is either not possible or suboptimal. No matter how far the digitalization of pathology progresses, microscopes and light microscopy are the basis of current pathologic evaluation and are here to stay. Maybe in the future, they will be available just as backup equipment, but in many regions of the world, from the rural mid-west of the United States to Malawi in Africa as the only tool of pathological evaluation. The fact that microscopes will remain in the pathology world along with the augmented reality units contributes to greater adoption of digital pathology even among the more skeptical pathologists, as well as empowers the institutions that cannot afford to go fully digital in their laboratories.
SUMMARY
In the era of personalized medicine, where doctors and pharmaceutical companies strive to provide individualized therapy for each patient based on their biomarker profiles, also digital pathology companies realized that the “one size fits all” approach is not the best model for pathologists. Every institution has its specific workflow and its challenges, and it is not possible to develop one product for everybody. The solutions designed for primary diagnosis in a very busy hospital usually don’t fit the flexibility and workflow requirements of a research or drug development support team.
To better address the customers’ needs paradigms in the digital pathology industry needed to shift:
- Image analysis companies replaced the classical hand-crafted image algorithms with trainabledeep learning methods.
- More and more companies are providing software as a service (Saas)instead of traditional computer-installed software.
- To enable users who do not possess enough computational power on their premises to run deep learning models efficiently, companies now offer cloud computing capabilities and cloud-based user interfaces.
- Companies, instead of designing software that makes the decisions for the users, now develop decision support systems, where pathologists and scientists have the final word and can accept or reject the results suggested by the software.
- Digital pathology and image analysis software has become more and more user-friendly.
These together with paying close attention to the users’ needs and working closely with them to improve existing products are yielding more and more personalized digital pathology, where the state of art computer vision and image analysis methods can be used flexibly and applied to a variety of pathology and scientific problems.
Original post published here: https://digitalpathologyplace.com/new-trends-and-recent-paradigms-shift-in-the-digital-pathology-industry/