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 based on a dual approach of both detecting hair follicles and estimating the local hair density in order to give accurate counts even for cases where two or more adjacent hairs are in close proximity to each other. RESULTS: We evaluate our system on 70 OCT manually labeled scans taken at different scalp locations from 14 patients, with 20 of those redundantly labeled by two human expert OCT operators. When comparing the individual human predictions and considering the exact locations of hair and follicle predictions, we find that the two human raters disagree with each other on approximately 22% of hairs and follicles. Overall, the deep learning (DL) system predicts the number of follicles with an error rate of 11.8% and the number of hairs with an error rate of 18.7% on average on the 70 scans. The OCT system can capture one scalp location in three seconds, and the DL model can make all predictions in less than a second after processing the scan, which takes half a minute using an unoptimized implementation. CONCLUSION: This approach is well-positioned to become the standard for non-invasive evaluation of hair growth treatment progress in patients, saving significant amounts of time and effort compared with manual evaluation. Lasers Surg. Med. © 2020 Wiley Periodicals, Inc.

Optical Coherence Tomography in the Evaluation of the Scalp and Hair: Common Features and Clinical Utility.

BACKGROUND AND OBJECTIVE: Early diagnosis and treatment of hair loss disorders is vital in providing patients with improved psychological outcomes. Non-invasive imaging with optical coherence tomography (OCT) may be useful in characterizing and managing alopecia. Despite expanding clinical applications of OCT in dermatology, guidelines demonstrating in vivo features of normal and alopecic scalp images remain scant. This pilot study aims to provide an atlas of OCT findings of healthy and alopecia subjects, explore diagnostic quantitative endpoints of alopecia, and compare epidermal thickness and follicular density between scalp regions. STUDY DESIGN/MATERIALS AND METHODS: A total of 32 patients (19-76 years old) were enrolled in the study, including healthy patients (n = 6), and patients with scarring alopecia (n = 12) or non-scarring alopecia (n = 14). An in-line fiber-based swept source OCT was used to image five scalp locations at baseline and 6-month visits. Three investigators evaluated each image for gross features, epidermal thickness, and follicular density. RESULTS: Only data from baseline imaging analysis is discussed in this manuscript. Qualitative differences of OCT images are identified in sample images from healthy scalp and each subtype of alopecia studied. Scarring alopecia is characterized by significantly increased epidermal thickness (average Image J pixel units 32 ± 2 compared with non-scarring alopecia [average 28 ± 3] and control [average 27 ± 3]) (P = 0.022) and decreased follicle count (average 35 ± 5 in a 5 × 7 mm2 area compared with control (50 ± 3) and non-scarring patients (47 ± 6)) (P = 0.0052). Scalp location had no impact on epidermal thickness (P = 0.861) or follicular density (P = 0.15). CONCLUSION: OCT holds promise as a non-invasive technique to further characterize and objectively measure alopecia. Larger sample sizes and longitudinal data are needed to improve reliability and determine if additional distinction between alopecia subtypes and treatment monitoring is possible. Lasers Surg. Med. © 2020 Wiley Periodicals, Inc.