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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.

For many clinical applications, such as dermatology, optical coherence tomography (OCT) suffers from limited penetration depth due primarily to the highly scattering nature of biological tissues. Here, we present a novel implementation of dual-axis optical coherence tomography (DA-OCT) that offers improved depth penetration in skin imaging at 1.3 µm compared to conventional OCT. Several unique aspects of DA-OCT are examined here, including the requirements for scattering properties to realize the improvement and the limited depth of focus (DOF) inherent to the technique. To overcome this limitation, our approach uses a tunable lens to coordinate focal plane selection with image acquisition to create an enhanced DOF for DA-OCT. This improvement in penetration depth is quantified experimentally against conventional on-axis OCT using tissue phantoms and mouse skin. The results presented here suggest the potential use of DA-OCT in situations where a high degree of scattering limits depth penetration in OCT imaging.