Joseph Mossel is the CEO of Ibex Medical Analytics. His profession within the tech industry spans greater than 20 years, commencing in software development and product management followed with leadership positions in startups, large multinational corporations and non-profits. Joseph has led products from inception all of the approach to maturity as multi-million-dollar businesses. He holds a MSc in computer science from Tel Aviv University, and a MSc in environmental science from VU Amsterdam.
Developed by pathologists for pathologists, Ibex is a clinical-grade, multi-tissue platform that helps pathologists detect and grade breast, prostate and gastric cancer, together with greater than 100 other clinically relevant features.
Seamlessly integrated with third party digital pathology software solutions, scanning platforms and laboratory information systems, Ibex’s AI-enabled workflows deliver automated high-quality insights that enhance patient safety, increase physician confidence and boost productivity.
What inspired you to co-found Ibex Medical Analytics (Ibex), and what problem were you aiming to unravel?
Cancer, unfortunately, touches everyone–whether or not they are personally affected, have been a caregiver for somebody with cancer, or know of somebody who has been impacted. I actually have relatives and friends who’ve been affected by cancer, and tragically, certainly one of our employees passed away from cancer.
As cancer incidence continues to rise worldwide, there’s an increasing demand for cancer diagnostics that’s being compounded by a world shortage of pathologists, whose jobs have gotten more complex with advances in therapy and a requirement for more complex diagnostics.
Our platform helps overcome these challenges by empowering pathologists with AI tools that enhance accuracy and streamline workflows to be certain that every patient receives an accurate and timely diagnosis, which is instrumental each in guiding treatment decisions and ultimately improving patient outcomes.
We’re happy with the work we do for our customers, a lot of whom depend on our technology each day to deliver higher diagnoses. Their trust in our solutions highlights the true impact we’re making, transforming the sector of pathology, and improving patient outcomes.
Are you able to share a bit about your background and the way it led to your work in AI-powered pathology?
If I look back at my profession, there have been two driving forces: a seek for a way of purpose and a preference for interdisciplinarity over deep specialization. I’m lucky to run an organization that offers me a deep sense of purpose and allows me to work with an incredibly talented team from diverse backgrounds and disciplines.
My original academic background was in computer science, specializing in computational neuroscience. I then worked as an algorithms engineer and moved on to product management. After a stint at a big corporation, I made a decision that it was not for me. I earned a level in environmental science and ran an environmental non-profit for several years. Sustainability stays a passion of mine and is taken into account the nice challenge of our time.
Around ten years ago, I met my co-founder, Chaim Linhart, who was equally driven to make a meaningful difference and shared my passion for technology. Chaim, unlike me, is a specialist. He has a PhD in computer science and greater than 25 years of experience in algorithm development, AI, and machine learning (ML). In the primary days of Ibex, Chaim was busy winning Kaggle (ML) competitions.
Once we learned that pathology is being (slowly) digitized, we talked concerning the impact a digital transformation in pathology could have on improving cancer diagnostics. A whole lot of firms were already developing AI in radiology, and we asked ourselves, why not do the identical in pathology? It gave the impression of a natural fit to bring our technological expertise into the sector, collaborating closely with pathologists every step of the best way.
What were a number of the biggest challenges you faced within the early days of Ibex, and the way did you overcome them?
The thought -which we weren’t the primary to return up with- of applying AI to pathology slides was the straightforward part. Execution is difficult. The three foremost challenges we encountered throughout the early days of Ibex were access to data, access to capital, and access to domain-specific knowledge.
We solved the information challenge through partnering with Maccabi Health Services of Israel. At that time, we were two fledgling entrepreneurs with no medical knowledge who decided to open a medical startup in a really complex domain. Still, Varda Shalev, who headed Maccabi’s innovation arm on the time, believed in our vision, and we signed a partnership and data-sharing agreement with Maccabi. At this point, Dr. Judith Sandbank, the chief pathologist at Ibex got here on board as our Chief Medical Officer (CMO), a position she still holds. With a strategic partner and a CMO, we were now well-positioned to boost a seed round, which we raised from Kamet Ventures, a French enterprise studio that was a part of AXA Insurance.
We were now positioned to make history. We hired two engineers and developed our first algorithm for prostate cancer detection. Once we were pleased with the performance, we deployed it on the Maccabi pathology lab as a second read, reviewing all the cases after an initial read by the pathologist. To our surprise, inside a number of days, the system raised an alert for a case of cancer that was missed by the pathologist. So far as we all know, this was the primary case ever where the initial diagnosis of cancer was made by an algorithm, back in 2018.
Congratulations on receiving FDA 510(k) clearance for Ibex Prostate Detect! What does this approval mean for Ibex and the broader field of AI-powered diagnostics?
Thanks! This approval marks a big milestone in Ibex’s journey and exemplifies our dedication to developing clinically validated solutions that help improve patient health outcomes. It affirms our commitment to the security and efficacy of our solutions and strengthens our ability to offer cutting-edge innovation to pathologists, ultimately benefiting the patients they serve.
We envision that this tremendous milestone will break down barriers and speed up the adoption of AI and digitization in pathology. We hope this accomplishment will bolster industry-wide confidence that the technology is simple to implement and prepared for wide-scale use. Long-term, FDA clearance is a very important step towards achieving reimbursement for AI in pathology and fostering widespread adoption.
The FDA validation process highlighted a 13% rate of missed cancers in initial benign diagnoses. What does this tell us concerning the potential of AI to enhance diagnostic accuracy?
Within the robust precision and clinical validation studies conducted at multiple United States and European laboratories as a part of the FDA clearance, the system identified a 13% rate of missed cancers in a cohort of consecutive patients initially diagnosed as benign. This statistic reinforces the accuracy and impact of Ibex’s products, and it also validates that Ibex’s AI platform could be integrated safely into clinical workflows, enhancing diagnostic precision and ultimately improving patient care. By providing a further layer of research, our technology helps to scale back errors, enable higher clinical decision-making, and promote patient safety.
As for potential, while the clearance serves as a critical validation of our technology, our solution has already been making a meaningful impact out there. This can be a testament to the each day exertions in pathology labs, and we see this as a step forward in improving health outcomes globally. We will’t help but imagine the impact this could have if labs across america embraced a digital transformation.
How does Ibex Prostate Detect work, and what makes it unique in comparison with other AI-driven pathology solutions?
Ibex Prostate Detect is an in vitro diagnostic medical device that harnesses AI to generate heatmaps identifying missed prostatic cancers. Acting as a security net, Ibex Prostate Detect assists pathologists in ensuring that patients receive an accurate diagnosis. It leverages AI algorithms to boost the accuracy of a prostate cancer diagnosis.
The device is meant to discover tumors which will have been missed by the pathologist. If suspicious tissue for prostate cancer is identified, the system generates an alert and features a heatmap, directing the pathologist to areas prone to contain cancer. Ibex Prostate Detect is the one FDA-cleared solution that gives AI-powered heatmaps for all areas with a likelihood of cancer, offering full explainability to the reviewing pathologist.
Are you able to explain how the heatmap feature assists pathologists in identifying cancerous tissue?
Ibex Prostate Detect is meant to discover cases initially diagnosed as benign for further review by a pathologist. If it detects tissue morphology suspicious for prostate adenocarcinoma (AdC), atypical small acinar proliferation (ASAP), and other rare cancer subtypes, it provides alerts that include a heatmap of tissue areas in the entire slide images that’s prone to contain cancer, offering full explainability to the reviewing pathologist.
Generally, the heatmap is accurate and precise and should provide the pathologist with areas of concern that they’ll give attention to and determine the right diagnosis. Within the precision and clinical validation studies conducted as a part of the FDA clearance, Ibex Prostate Detect’s heatmaps demonstrated extreme pixel accuracy and determined the next:
- Nearly all cancer areas are covered by the heatmap (sensitivity=98.7%).
- Almost all the things highlighted as high probability of cancer within the heatmap is indeed cancer (PPV=99.6%).
- The missed cancer cases (false negatives) identified by the system were subsequently verified by expert pathologists, confirming the product’s clinical utility and advantages compared with the present standard of care.
How does the AI model differentiate between benign and malignant tissue, and the way was it trained?
The Deep Learning algorithm relies on multilayered convolutional neural networks, operating on several magnification levels. The AI is exceptionally robust, demonstrating high accuracy across multiple labs and patient demographics. Of note, in step with our mantra of ‘by pathologists, for pathologists,’ the model was trained on over 1,000,000 slides painstakingly annotated by world-renowned pathologists at leading medical centers. This approach is dear, but we consider that without the insight of pathologists it is vitally difficult to achieve the extent of performance we’re aiming for. By doing this, we equip all pathologists with expert insights and be certain that every patient, no matter their location, receives a level of diagnosis on par with the world’s leading specialists.
Beyond prostate cancer, Ibex can be working on solutions for breast and gastric cancers. What’s next for the corporate by way of latest diagnostic capabilities?
Ibex is already having a huge effect on AI-powered diagnostic solutions for breast and gastric cancers. Because the worldwide leader in live clinical rollouts, many labs – including those in america – are already using Ibex products to rework their medical practice. Our products are proven to deliver real-world clinical impact, and pathologists each trust the AI and attest to the worth it brings. Now, we’re working to release a brand new style of technology into the market, a technology that was developed and validated by Ibex in collaboration with AstraZeneca and Daiichi Sankyo. The precise algorithm that’s the first to be released helps quantify HER2 expression, which helps providers determine the course of treatment for the patient.
Looking ahead, we’ll proceed to expand our offerings to offer additional insights throughout the tissue types we already support. We’re also trying to provide offerings inside other tissue areas and proceed improving our customers’ workflows.
How do you see AI-powered pathology evolving in the following five to 10 years?
I envision that AI could have a profound impact on the practice of pathology and the best way cancer is diagnosed. I don’t see us replacing pathologists, but as with every latest technological development, the practice shall be transformed. AI will proceed to be instrumental in addressing the growing workforce challenges in healthcare, particularly the worldwide shortage of pathologists and their increasing caseloads driven by rising cancer cases. Implementing responsible AI will help pathologists manage their workloads more effectively, improving diagnostic efficiency and reducing delays. By automating routine tasks, AI can lower error rates, improve the standard of diagnosis, and ultimately boost pathologists’ confidence of their work. I strongly feel that AI, along with a human within the loop, is the perfect combination for transforming healthcare.
One other area with great promise is expanding beyond the present practice of pathology into the realm of predictive algorithms. Algorithms that potentially mix several modalities to predict outcomes or, crucially, treatment efficacy.
AI may enhance health equity through democratized health access. No matter location, every patient, in every single place deserves a trusted diagnosis. It will be great for AI technology to be deployed as part of normal practice in every pathology lab worldwide. Nevertheless, this starts with collaboration amongst physicians, the industry, and agencies to speed up the deployment of this technology–I feel we owe it to patients.
