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NEW AI DIVISION
AT DUKE, THE FUTURE OF PATHOLOGY LIES IN AI
Duke Engineering and the Department of Pathology in the School of Medicine have joined forces to create a new Division of Artificial Intelligence and Computational Pathology.
Researchers at Duke University have been merging artificial intelligence (AI) with health care to improve patient outcomes for the better part of two decades. From making cochlear implants deliver purer sounds to the brain to finding hidden trends within reams of patient data, the field spans a diverse range of niches that are now beginning to make real impacts. Among these niches, however, there is one in which Duke researchers have always been at the leading edge: image analysis, with a broad team of researchers teaching computers to analyze images to unearth everything from various forms of cancer to biomarkers of Alzheimer’s disease in the retina.
To keep pushing the envelope in this field by cementing these relationships into both schools’ organization, Duke’s Pratt School of Engineering and Department of Pathology have launched a new Division of Artificial Intelligence and Computational Pathology. “Machine learning can do a better job than the average person at finding the signal in the noise, and that can translate into better outcomes and more cost-effective care,” said Michael Datto, Associate Professor and Associate Vice President for Duke University Health System Clinical Laboratories. “This is one of the most exciting times I’ve seen in Pathology, and it’s going to be exciting to see what we can do.”
The new division will support translational research by developing AI technologies for image analysis to enhance the diagnosis, classification, prediction and prognostication of a variety of diseases, as well as train the next generation of pathologists and scientists in the emerging field. The division is led by Carolyn Glass, Assistant Professor of Pathology, and Laura Barisoni, Professor of Pathology and Medicine, and operates with the partnership of AI Health, directed by Lawrence Carin, Professor of Electrical and Computer Engineering (ECE) and Vice President for Research at Duke, and Adrian Hernandez, Professor of Medicine and Vice Dean for Clinical Research.
“Duke has taken the lead at the national level in establishing a division in the Department of Pathology in partnership with AI Health with the goal of developing and establishing new models and protocols to practice pathology in the 21st century,” said Barisoni, who is also Director of the Renal Pathology service at Duke. AI Health is also a new initiative, launched as a collaboration between the Schools of Engineering and Medicine and Trinity College of Arts & Sciences, with units such as the Duke Global Health Institute and the Duke-Margolis Center for Health Policy, to leverage machine learning to improve both individual and population health through education, research and patientcare projects. “For what everyone has envisioned for AI Health, we see Pathology paving the way,” said Hernandez. “AI Health is a catalyst and spark for putting cutting-edge machine learning development and testing into real world settings. In Pathology, we have imageintensive data streams, and COVID-19 has really emphasized the need for the timely processing of patient samples.”
Applying machine learning image analysis to pathology processes, however, is easier said than done. Figuring out how to process extremely large image files and train AI algorithms on relatively few examples is part of the focus of Carin’s laboratory, in partnership with Ricardo Henao, Assistant Professor of Biostatistics and Bioinformatics as well as ECE.
Current AI algorithms, such as convolutional neural networks (CNN), were originally designed for the analysis of natural images, such as those captured on phones. Adapting such algorithms for the diagnosis of biopsy scans, however, is challenging due to the large size of the scans—typically of tens of gigabytes—and the sparsity of abnormal diagnostic cells they contain. Led by David Dov, a postdoctoral researcher in Carin’s laboratory, Duke engineers are working to overcome these challenges to design AI algorithms for the diagnosis ofvarious conditions, such as different types of cancers and transplant rejection.
“Designing algorithms that make a real impact on clinical practice requires close collaboration between AI researchers and pathologists,” said Dov, who joined Duke after completing his PhD in electrical engineering at The Technion - Israel Institute of Technology. “A key challenge in these collaborations is gaining a deep understanding of the gaps in medical practice, and then ensuring that clinicians fully understand the capabilities and limitations of AI in bridging these gaps. The new Division of Artificial Intelligence and Computational Pathology plays an important role in facilitating such collaborations.”
In a virtual kickoff meeting this fall, the new division’s leadership spoke to the potential it holds to improve patient health outcomes and several researchers delved into projects they already have underway in the field. For example, Danielle Range, Assistant Professor of Pathology, spoke of efforts to use AI in diagnosing cancer; Roarke Horstmeyer, Assistant Professor of Biomedical Engineering, described his efforts to create a “smart microscope” to better diagnose disease; and Glass detailed her work on the use of machine learning in diagnosing transplant rejection. “In the last couple of years, we have seen an exponential increase in AI pathology interest from Duke undergraduates to medical students applying for Pathology residency positions,” said Glass. “I think continued development of a solid, integrated curriculum and educational program will be critical to train these future leaders.”
In a virtual kickoff meeting this fall, the new division’s leadership spoke to the potential it holds to improve patient health outcomes
--Pathology Projects--
Artificial intelligence (AI) does not subtract human input or eliminate doctors. Rather, it multiplies the knowledge doctors have to address diseases and provide treatment options, creating an equation that improves patient care.
A NEW DIVISION, AN EXCITING ADDITION
In early 2020, the newly-formed Duke Division of AI & Computational Pathology (DDAICP) hit the ground running. Their accomplishments already span a wide swath: under the leadership of pathologists Dr. Laura Barisoni and Dr. Carolyn Glass, the team built interdisciplinary collaborations between Duke University, the School of Medicine, industry, and other academic institutions. These collaborations have resulted in presentations at national and international meetings, manuscripts in high-impact journals, and federal grant submissions. Research projects are supported by internal grants including the Duke Coulter Translational Award and a donation by the Nephcure Foundation. In addition, Duke pathologists Dr. Rajesh Dash and Dr. Glass, members of the DDAICP, were selected for leadership positions in the College of American Pathology AI Committee, which shapes AI policy at the national level.
Future AI leaders are already in the education system. Recognizing that computational pathology and image analysis will be integrated in patient care, Duke Pathology strives to be at the forefront, modernizing the curriculum for pathology trainees with the introduction of formal lectures that cover the basic aspects of digital pathology, image analysis, regulatory issues, and clinical applications. Growing interest in computational pathology among the new generation of pathologists is reflected by the high level of pathology residents’ participation in image analysis projects, providing exposure to interdisciplinary collaborations and the opportunity to present theirfindings at national scientific meetings.
Dr. Will Jeck is working with AI to detect epithelial structures, shown here in green, in a small bowel transplant biopsy. This is a preliminary step in the detection of apoptotic cell density, a diagnostic feature of transplant rejection.
Photo: Submitted
Gina Sotolongo, MD, a second-year Duke resident and new Chief resident, has explored tools for the automatic detection of inflammatory cells in biopsies. She describes her experience working with the pathology AI team: “I learned that AI is not magic; if it is difficult for a pathologist to visually tell the difference (without the help of molecular or stains), AI will have difficulty as well. When working on AI projects, patience, good communication and a willingness to learn from your computer engineering colleagues are all important for success.” She intends to do more work with AI. “AI is the future of Pathology. Duke is ahead of the curve by having an AI division — it makes Duke stand out.” Dr. Sotolongo’s project is in partnership with Duke undergraduate engineering student Jihyeon Je; both are co-mentored by Drs. Lafata and Barisoni.
Richard Davis, MD MSPH, a third-year resident who has contributed to three analytics projects, describes his image analysis project on donor kidney biopsies: “We have been trying to quantify glomeruli and determine whether they are nonsclerotic or globally sclerotic, as part of the treatment decision for whether or not a kidney is going to be implanted into a recipient. Our research manuscript is now under review for publication.” He is co-first author with Xiang Li, a graduate in Duke’s Electrical and Computer Engineering (ECE) program. The close cross-disciplinary collaboration with Xiang and Kyle Lafata, PhD, Assistant Professor of Radiology, Radiation Oncology, and ECE, has been essential for the success of this project. “I thought there would be projects out there, but not to this scale or degree of collaboration with our colleagues in different departments. I feel the opportunities are very unique to Duke.”
Bangchen Wang, MD PhD, a first-year Pathology resident, is working in close collaboration with computer scientists at Duke and Case Western Reserve University to develop protocols for automatic detection of tubules in kidney biopsies. “I’m interested in both kidney diseases and AI, so this project is a perfect combination,” he said. “By integrating clinical, histological and molecular data, AI can also provide valuable insights to the diagnosis, prognosis, and treatment of diseases. In a sense, Pathology is data science.”
Other projects include the work of Neuropathology Fellow Patricia Pittman, MD, with COVID diagnostics analytics.
Another exciting development is the creation of a fellowship. The DDAICP, in partnership with AI Health, has recruited its first AI Pathology Fellow for the two-year training program. Starting in spring 2021, Akhil Ambekar, MS will conduct research on three projects, including kidney transplant, lung cancer, and gastrointestinal pathology.
The Duke Division of AI and Computational Pathology has already achieved recognition regionally and nationally in thyroid cancer, prostate cancer, cardiac, lung and kidney transplantation, non-neoplastic renal disease, infectious disease (COVID) and hematologic disorders. Future investigations will include immunology, lung cancer, mesothelioma, liver disease, gastrointestinal disease and melanoma.
The Division extends an invitation for new faculty members and looks forward to nurturing ideas and collaborations, multiplying the options in the equation to improve personalized patient care. Please see https://pathology.duke.edu/patient-care/anatomic-pathology/ai-and-computational-pathology.
Annotations made by a cardiac transplant pathologist to train the algorithm to detect between myocyte damage or non-damage. (Orange:likely damage; Yellow: likely no damage)
Photo: Submitted
A heat map shows how glomeruli from frozen sections of donor kidney biopsies can be clustered based on common pathomic signatures.
Photo: Submitted
