Application of neural networks on in vitro-generated Raman spectra for label-free, ex vivo skeletal muscle detection. (30th November 2022)

• Record Type: Journal Article
• Authors: Cheng, Chien Ting; Wang, Dan; Lee, Oscar Kuang-Sheng; Wang, Huating; Ker, Dai Fei Elmer
• Is Part Of: Measurement. Volume 204(2023)
• Publication Date: 2022-11-30
• Subjects: Artificial intelligence -- Convolutional neural networks -- In vitro models -- Label-free detection -- Raman spectroscopy -- Skeletal muscle detection; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112172
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Novel use of in vitro models to augment sample numbers for improved artificial intelligence-aided Raman analysis. Raman spectra generated during in vitro myogenesis recapitulates aspects of ex vivo skeletal muscle samples. Achieved 98.5% detection accuracy for in vitro myogenesis. Achieved 85.0% detection accuracy for ex vivo skeletal muscle using in vitro -generated Raman data. Abstract: Raman spectroscopy can detect sample molecular changes in a label-free manner but the scarce nature of precious biomedical samples and their high spectral complexity impede use of artificial intelligence (AI) for accurate analysis and diagnostics. To address this, we assessed whether in vitro -generated Raman spectra can aid AI-based neural network learning for accurate ex vivo skeletal muscle detection, as a proof-of-concept. First, a diverse in vitro Raman spectral dataset was generated followed by identification of myogenic peaks. Second, myogenic peak assignment was validated by comparison with skeletal muscle tissue. Lastly, 1-dimension (1D) convolutional neural networks (CNNs) trained from in vitro- generated Raman spectra attained 98.5% and 85.0% detection accuracy for in vitro myogenesis and ex vivo skeletal muscle, respectively. In conclusion, we demonstrated proof-of-concept for using in vitro generated data for ex vivo label-free tissue detection, which may be broadly applied to facilitate label-free Raman detection for research and clinical diagnostics.