Papers Published

1. Zhang, H; Kendall, WY; Jelly, ET; Wax, A, Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles., Biomedical Optics Express, vol. 12 no. 8 (August, 2021), pp. 4997-5007 [doi] .
   (last updated on 2023/06/01)

   Abstract:
   We present a machine learning method for detecting and staging cervical dysplastic tissue using light scattering data based on a convolutional neural network (CNN) architecture. Depth-resolved angular scattering measurements from two clinical trials were used to generate independent training and validation sets as input of our model. We report 90.3% sensitivity, 85.7% specificity, and 87.5% accuracy in classifying cervical dysplasia, showing the uniformity of classification of a/LCI scans across different instruments. Further, our deep learning approach significantly improved processing speeds over the traditional Mie theory inverse light scattering analysis (ILSA) method, with a hundredfold reduction in processing time, offering a promising approach for a/LCI in the clinic for assessing cervical dysplasia.