  Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has
recently demonstrated its potential for accurate diagnosis of human cervical
diseases. One major challenge for clinical adoption, however, is the steep
learning curve clinicians need to overcome to interpret OCM images. Developing
an intelligent technique for computer-aided diagnosis (CADx) to accurately
interpret OCM images will facilitate clinical adoption of the technology and
improve patient care. Methods: 497 high-resolution 3-D OCM volumes (600
cross-sectional images each) were collected from 159 ex vivo specimens of 92
female patients. OCM image features were extracted using a convolutional neural
network (CNN) model, concatenated with patient information (e.g., age, HPV
results), and classified using a support vector machine classifier. Ten-fold
cross-validations were utilized to test the performance of the CADx method in a
five-class classification task and a binary classification task. Results: An
88.3 plus or minus 4.9% classification accuracy was achieved for five
fine-grained classes of cervical tissue, namely normal, ectropion, low-grade
and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In
the binary classification task (low-risk [normal, ectropion and LSIL] vs.
high-risk [HSIL and cancer]), the CADx method achieved an area-under-the-curve
(AUC) value of 0.959 with an 86.7 plus or minus 11.4% sensitivity and 93.5 plus
or minus 3.8% specificity. Conclusion: The proposed deep-learning based CADx
method outperformed three human experts. It was also able to identify
morphological characteristics in OCM images that were consistent with
histopathological interpretations. Significance: Label-free OCM imaging,
combined with deep-learning based CADx methods, hold a great promise to be used
in clinical settings for the effective screening and diagnosis of cervical
diseases.
