J Biophotonics. 2023 Aug 14:e202300015. doi: 10.1002/jbio.202300015. Online ahead of print.

ABSTRACT

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 cm^-1 to 850 cm^-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data. This article is protected by copyright. All rights reserved.

PMID:37578837 | DOI:10.1002/jbio.202300015