Ibex’s Daphna Laifenfeld On AI-Powered Cancer Diagnostics, Company Raising $38 Million In Series B Funding And The Future Of Personalized Medicine
AI, certainly, appears to be the following nice wave of transformation in healthcare. Countries worldwide are challenged by fixed development, and rising complexity, of their healthcare wants with an getting old inhabitants and improve in illness incidence. Among probably the most promising medical purposes of AI is diagnostic imaging, and mounting consideration is being directed at establishing and fine-tuning its efficiency to facilitate detection and quantification of a wide selection of medical circumstances. AI carries large promise for healthcare, with purposes that already present advantages to sufferers in fields corresponding to telehealth, personalised drugs, screening, most cancers diagnostics, and high quality management, to call just some. The pandemic additional crystallized the necessity to speed up the adoption of latest applied sciences that facilitate telehealth, corresponding to most cancers prognosis performed remotely.
IDTechEx expects the marketplace for AI-enabled image-based medical diagnostics to develop by almost 10,000% till 2040 and the marketplace for AI-enabled image-based medical diagnostics to exceed $3 billion by 2030 throughout areas corresponding to most cancers, cardiovascular illnesses, respiratory, retinal, and neurodegenerative illnesses.
Pioneers In Cancer Diagnostics
Founded in 2016, Ibex Medical Analytics pioneered most cancers diagnostics in pathology and is the primary and to date the one firm to deploy reside AI options in laboratories and hospitals the world over, with concrete proof displaying that its personal resolution helps physicians enhance the standard and velocity of most cancers prognosis. Simply put, Ibex makes use of synthetic intelligence to assist physicians and healthcare suppliers rework most cancers prognosis. Its workforce of pc scientists and machine studying consultants develops AI algorithms that mimic the work of a pathologist and detect, at a really excessive diploma of accuracy, most cancers and different medical options in tissue biopsies. Such algorithms are then utilized in labs around the globe to investigate reside instances and supply an automatic, AI-based “second opinion” that alerts when detecting cancers that have been missed by pathologists. This manner, AI is used as a “safety net” for pathologists and sufferers and helps in decreasing, and almost eliminating, errors and misdiagnosis.
“Another way pathologists can use our AI technology is as a tool for decision support, a kind of “trusted advisor” that helps them kind by means of instances and full diagnoses a lot quicker. With the worldwide improve in most cancers incidence placing fixed stress on diagnostics labs, AI might make pathologists significantly extra productive and stay targeted on the extra complicated instances, an enormous profit to well being programs that wrestle to keep up their service ranges,” Dr. Daphna Laifenfeld, Ibex’s Chief Scientific Officer shares with me in an interview.
Laifenfeld has devoted her profession to the sector of personalised drugs, starting along with her tenure in academia (on the Technion–Israel Institute of Technology and Harvard Medical School) the place she targeted on pathways underlying neuropsychiatric problems corresponding to main melancholy and Alzheimer’s illness. She rapidly got here to acknowledge that personalised drugs is inherently concerning the intersection between drugs, its underlying knowledge, and expertise – an method she applied in numerous business positions and as a co-founder of GenetikaPlus, specializing in personalised drugs in Depression. Daphna joined Ibex after having headed Diagnostics and Personalized Medicine at Teva Pharmaceuticals. Her data, ardour, and expertise in utilizing expertise to progress medical apply for speedy impression on affected person lives by means of AI-based diagnostics are very apparent, and she or he is a type of individuals who can clarify scientifically difficult issues in a quite simple manner (at the least for us with no scientific background, that’s).
The Power Of An AI Algorithm
Ibex’s platform is utilized by pathologists – the physicians tasked with diagnosing numerous illnesses together with most cancers, who sometimes work in pathology laboratories present in massive medical facilities, neighborhood hospitals, and the personal sector. As Laifenfeld highlights – “an AI system is only as good as the data used to train it”. That’s why Ibex has partnered with Maccabi Healthcare Services, a big healthcare supplier in Israel that owns one of many largest digitized medical datasets on this planet. Maccabi’s archives embrace thousands and thousands of pathology slides and totally digitized pathology studies – an actual goldmine for builders of AI algorithms for pathology.
“We augment the Maccabi dataset with datasets from other pathology institutes and work with pathologists who manually annotate biopsy images. These annotations are used during the training phase, resulting in a model which is then tested on a new set of images and compared to what we call ‘ground truth’, typically determined by other pathologists. It’s an iterative and meticulous process that ends only after rigorous validation with independent pathologists that determine that the algorithm is accurate and meets its performance goals,” she explains.
“At the end of the day, your algorithm relies heavily on the quality of the dataset but more importantly, it’s the know-how and skills of the development team, as well as the methodology they chose, that determine the quality of your AI. One important characteristic of our R&D process is the fact that we engage pathologists throughout the entire product development cycle. They help us adopt the clinician’s point of view on what’s important and how they are able to detect, with their eyes, of course, specific features, for example, cancer in a tissue sample. It’s our understanding of their thought process, which helps tremendously in developing an algorithm that is supposed to mimic their work. We also decided very early on to develop Strong AI algorithms – these are algorithms trained to perform more than one task, or in our field – detect more than just cancer. There are many non-cancerous features that pathologists are trained and actually required to detect and report. Training our algorithms to detect many types of features has improved their accuracy in detecting cancer as well and helped pathologists embrace our solutions more easily,” she continues.
Some of Ibex’s main successes embrace growing a primary resolution for detecting prostate most cancers, which they deployed initially at Maccabi Healthcare Services. Its AI proved its utility inside weeks when it alerted on most cancers that was missed by pathologists – the first-ever reported case of a misdiagnosed most cancers that was detected in real-time by an AI resolution. Since then the corporate has deployed its AI resolution in labs the world over they usually routinely detect missed cancers – ensuring that the case is reviewed and corrected. Laifenfeld and her workforce additionally continued R&D efforts, including an answer for breast most cancers, making Ibex’s platform the first-ever multi-tissue AI resolution (for breast and prostate) deployed in routine apply in pathology.
“We are now working on solutions for additional tissue types, expected to hit the market later this year, as well as for new applications for additional workflows in pathology that have already demonstrated significant productivity gains. Finally, we are engaged with several partners on projects for the development of AI-markers for prognostic and predictive applications used in cancer management and drug development,” provides Laifenfeld.
The U.Okay. is on the forefront of fixing most cancers care, in line with Laifenfeld, and it’s led by ground-breaking, U.Okay. government-supported digital pathology and AI initiatives corresponding to PathLAKE+, NPIC, and iCAIRD. This is a chance for Ibex to implement clinical-grade AI throughout totally digitized pathology networks deployed in a number of areas.
“We started off in the U.K. by teaming with LDPath, a London-based provider of digital pathology services to non-less than 24 NHS trusts throughout the majority of the U.K., including large teaching hospitals and district general hospitals. At the same time, we’ve created a partnership with major teaching hospitals, led by Imperial College London, and won a share of a £50 million fund as part of the U.K.’s AI in Health and Care Award, an initiative led by NHSx and the National Institute for Health Research (NIHR). This project, which is rolling out this year, will enable the deployment of Ibex’s AI platform in six NHS trusts and involves researchers from Imperial College London, University College London, University Hospitals Coventry & Warwickshire, and other institutes. It will enable the demonstration of the benefits of broad-scale implementation of AI technology to cancer patients.”
A $38 Million Series B Financing Found
Last week, Ibex introduced a $38 million Series B financing spherical led by Octopus Ventures and 83North, with extra participation from aMoon, Planven Entrepreneur Ventures, and Dell Technologies Capital, the company enterprise arm of Dell Technologies. The funding brings whole funding of Ibex to $52 million since its founding in 2016 by Joseph Mossel and Dr. Chaim Linhart. This new funding will assist the corporate meet the rising demand for AI and digital pathology rollouts, assist an increasing buyer base and develop expertise throughout its groups. “We intend to expand the Galen™ solution portfolio at Ibex, bringing new AI tools for more tissue types, including novel AI-based enhancements of the pathology workflow and oncology-focused AI-markers,” provides Laifenfeld.
“Raising money during a once-in-a-century pandemic is indeed an interesting experience. If you had told me a year ago that we would meet new investors, pitch the technology, prove our business case, develop necessary trust and finally ink $38 million in funding – all without boarding a plane even once, I would have thought you are out of your mind. But necessity is the mother of invention, and we all had to get used to doing things differently. We were confident in our vision and ability to execute on it, and the fact that our team continued working in full force throughout 2020 without major impediments helped us in gaining the trust of our investors. As a side note, it is worth mentioning that the pandemic accelerated the pace of digital transformation, particularly in healthcare. From this perspective, it was easier to make the case for a tech venture that helps physicians with remote work and provides efficiencies,” concludes Laifenfeld.