Alex Weston at the Breath Biopsy Conference 2022

[54 mins] KEYNOTE: Machine learning analysis of volatomic profiles in breath can identify non-invasive biomarkers of liver diesease

00:00 Introduction

1:00 Talk

37:40 Q&A

Talk Abstract:

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. The models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. A deep-neural network was  then optimized to discriminate between healthy controls and individuals with cirrhosis. The 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. In conclusion, CNNs demonstrate the ability to predict the presence of cirrhosis based on volatolomic profiling of patient breath samples.


Speaker Biography:

Alexander Weston is a Senior Data Science Analyst in the Digital Innovation Lab at Mayo Clinic, Jacksonville, FL. He received his PhD in Biomedical Engineering from Mayo Clinic Graduate School of Biomedical Sciences. His background includes medical imaging and machine learning model development. His research interests include building practical deep-learning based models which can quantify novel predictors of health from medical imaging and signals data.

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