Artificial Intelligence Advances in Transplant Pathology.

Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.

Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model.

Aims: Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results: Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78-0.90] and 95% (19/20; 95% CI: 75-100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61-0.96) and 100% (2/2; 95% CI: 16-100%) sensitivity. Conclusion: An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

Development and use of a genitourinary pathology digital teaching set for trainee education.

BACKGROUND: Automated, high-speed, high-resolution whole slide imaging (WSI) robots are becoming increasingly robust and capable. This technology has started to have a significant impact on pathology practice in various aspects including resident education. To be sufficient and adequate, training in pathology requires gaining broad exposure to various diagnostic patterns through teaching sets, which are traditionally composed of glass slides. METHODS: A teaching set of over 295 glass slides has been used for resident training at the Division of Genitourinary Pathology, Department of Pathology, University of Pittsburgh Medical Center. Whole slide images were prepared from these slides using an Aperio ScanScope CS scanner. These images and case-related information were uploaded on a web-based digital teaching model. RESULTS: THE WEB SITE IS AVAILABLE AT: https://www.secure.opi.upmc.edu/genitourinary/index.cfm. Once logged in, users can view the list of cases, or search cases with or without diagnoses shown. Each case can be accessed through an option button, where the clinical history, gross findings are initially shown. Whole slide images can be accessed through the links on the page, which allows users to make diagnoses on their own. More information including final diagnosis will display when the diagnosis-button is clicked. CONCLUSION: The web-based digital study set provides additional educational benefits to using glass slides. Residents or other users can remotely access whole slide images and related information at their convenience. Searching and sorting functions and self-testing mode allow a more targeted study. It would also prepare residents with competence to work with whole slide images. Further, the model can be expanded to include pre-rotation and post-rotation exams, and/or a virtual rotation system, which may potentially make standardization of pathology resident training possible in the future.