Precision Diagnostics of Donor Lungs Ex Vivo

Ex vivo lung perfusion (EVLP) is an advanced technology that reconditions donor lungs prior to transplantation. Lung monitoring during EVLP provides isolated lung data without confounding factors from other physiological systems. Herein, we used machine learning modelling to process EVLP diagnostic data and predict lung transplant outcomes. For functional data analysis, we first validated sampling methods for many biomarker assessments, by measuring biopsy mRNA and perfusate protein levels of inflammatory biomarkers from donor lungs declined for transplantation. Biopsy and perfusate samples across different locations were indeed representative of the whole lung, except for biopsies taken from the lingula or from lungs with gross focal injury. From there, we built an XGBoost algorithm and showed that lung functional data from clinical EVLP were highly predictive of transplant outcomes (transplanted lungs with <72h vs. ≥72h of recipient ventilation vs. declined lungs). We further investigated X-ray images acquired during clinical EVLP, which is another important assessment regularly performed in the Toronto protocol. We first established a standardized scoring method, analyzed findings in clinical ex vivo lung radiographs, and then developed a convolutional neural network (CNN) pipeline to simultaneously process temporal radiographs from different time points. We demonstrated the value of evaluating EVLP radiographs by showing that consolidation and infiltrate scores were indicative of lung injury. Moreover, automatically extracted radiographic features from our CNN strongly correlated with clinical consolidation and infiltrate findings, indicating that the trained CNN learned relevant information from clinical EVLP radiographs. The final, multi-modal model combining radiographic features and functional data significantly improved transplant outcome predictions. These foundational analyses and machine learning modelling of EVLP functional data and radiographs demonstrated the predictive value of isolated donor lung evaluations. In a high-intensity environment like lung transplantation where large amounts of data are constantly generated, these models can be readily deployed to support clinicians with accurate diagnostic information for more informed decisions.

Department dc:contributor.department

Biomedical Engineering

Year dc:date.issued

2024

Author dc:creator

• Chao, Bonnie Tso-Yu

Advisor dc:contributor.advisor

• Keshavjee, Shaf

• Artificial intelligence
• Ex vivo lung perfusion
• Isolated lung data
• Isolated lung radiographs
• Lung transplantation
• Transplant outcome modelling

Statement dc:rights

• Attribution-ShareAlike 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-sa/4.0/

Handle dc:identifier.uri

http://hdl.handle.net/1807/139925

OAI identifier oai:identifier

oai:utoronto.scholaris.ca:1807/139925