1. Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts

    Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts

    Background and Objectives One of the challenges in developing effective hair loss therapies is the lack of reliable methods to monitor treatment response or alopecia progression. In this study, we propose the use of optical coherence tomography (OCT) and automated deep learning to non‐invasively evaluate hair and follicle counts that may be used to monitor the success of hair growth therapy more accurately and efficiently. Study Design/Materials and Methods We collected 70 OCT scans from 14 patients with alopecia and trained a convolutional neural network (CNN) to automatically count all follicles present in the scans. The model is ...

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