Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia

Authors

  • Meng Gao Institute of Dermatology and Hospital for Skin Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College
  • Yue Wang
  • Haipeng Xu
  • Congcong Xu
  • Xianhong Yang
  • Jin Nie
  • Ziye Zhang
  • Zhixuan Li
  • Wei Hou Hospital of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, #12 Jiangwangmiao Road, Nanjing, Jiangsu, China
  • Yiqun Jiang Hospital of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, #12 Jiangwangmiao Road, Nanjing, Jiangsu, China

DOI:

https://doi.org/10.2340/actadv.v101.564

Keywords:

deep learning, androgenetic alopecia, trichoscopic image, BASP classification

Abstract

Since the results of basic and specific classification in male androgenetic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a deep learning framework for automatic trichoscopic image analysis and a quantitative model for predicting basic and specific classification in male androgenetic alopecia. A total of 2,910 trichoscopic images were collected and a deep learning framework was created on convolutional neural networks. Based on the trichoscopic data provided by the framework, correlations with basic and specific classification were analysed and a quantitative model was developed for predicting basic and specific classification using multiple ordinal logistic regression. A deep learning framework that can accurately analyse hair density and diameter distribution on trichoscopic images and a quantitative model for predicting basic and specific classification in male androgenetic alopecia were established.

Downloads

Download data is not yet available.

References

Lolli F, Pallotti F, Rossi A, Fortuna MC, Caro G, Lenzi A, et al. Androgenetic alopecia: a review. Endocrine 2017; 57: 9-17.

https://doi.org/10.1007/s12020-017-1280-y DOI: https://doi.org/10.1007/s12020-017-1280-y

Ellis JA, Sinclair R, Harrap SB. Androgenetic alopecia: pathogenesis and potential for therapy. Expert Rev Mol Med 2002; 4: 1-11.

https://doi.org/10.1017/S1462399402005112 DOI: https://doi.org/10.1017/S1462399402005112

Hamilton JB. Patterned loss of hair in man; types and incidence. Ann N Y Acad Sci 1951; 53: 708-728.

https://doi.org/10.1111/j.1749-6632.1951.tb31971.x DOI: https://doi.org/10.1111/j.1749-6632.1951.tb31971.x

Lee WS, Ro BI, Hong SP, Bak H, Sim WY, Kim DW, et al. A new classification of pattern hair loss that is universal for men and women: basic and specific (BASP) classification. J Am Acad Dermatol 2007; 57: 37-46.

https://doi.org/10.1016/j.jaad.2006.12.029 DOI: https://doi.org/10.1016/j.jaad.2006.12.029

Ishino A, Takahashi T, Suzuki J, Nakazawa Y, Iwabuchi T, Tajima M. Contribution of hair density and hair diameter to the appearance and progression of androgenetic alopecia in Japanese men. Br J Dermatol 2014; 171: 1052-1059.

https://doi.org/10.1111/bjd.13230 DOI: https://doi.org/10.1111/bjd.13230

Hayashi S, Miyamoto I, Takeda K. Measurement of human hair growth by optical microscopy and image analysis. Br J Dermatol 1991; 125: 123-129.

https://doi.org/10.1111/j.1365-2133.1991.tb06058.x DOI: https://doi.org/10.1111/j.1365-2133.1991.tb06058.x

Van Neste D, Trüeb RM. Critical study of hair growth analysis with computer-assisted methods. J Eur Acad Dermatol Venereol 2006; 20: 578-583.

https://doi.org/10.1111/j.1468-3083.2006.01568.x DOI: https://doi.org/10.1111/j.1468-3083.2006.01568.x

Saraogi PP, Dhurat RS. Automated digital image analysis (TrichoScan®) for human hair growth analysis: ease versus errors. Int J Trichology 2010; 2: 5-13.

https://doi.org/10.4103/0974-7753.66905 DOI: https://doi.org/10.4103/0974-7753.66905

Du-Harpur X, Watt FM. What is AI? Applications of artificial intelligence to dermatology. Br J Dermatol 2020; 183: 423-430.

https://doi.org/10.1111/bjd.18880 DOI: https://doi.org/10.1111/bjd.18880

Cullell-Dalmau M, Otero-Vinas M, Manzo C. Research techniques made simple: deep learning for the classification of dermatological images. J Invest Dermatol 2020; 140: 507-514.e501.

https://doi.org/10.1016/j.jid.2019.12.029 DOI: https://doi.org/10.1016/j.jid.2019.12.029

Jiang YQ, Xiong JH, Li HY, Yang XH, Yu WT, Gao M, et al. Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network. Br J Dermatol 2020; 182: 754-762.

https://doi.org/10.1111/bjd.18026 DOI: https://doi.org/10.1111/bjd.18026

Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017; 39: 640-651.

https://doi.org/10.1109/TPAMI.2016.2572683 DOI: https://doi.org/10.1109/TPAMI.2016.2572683

Yan LC, Yoshua B, Geoffrey H. Deep learning. Nature 2015; 521: 436-444.

https://doi.org/10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. [accessed 10th June 2019]. Available from: https://arxiv.org/abs/1704.04861.

Schwarz MW, Cowan WB, Beatty JC. An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models. ACM Transactions on Graphics (TOG) 1987; 6: 123-158.

https://doi.org/10.1145/31336.31338 DOI: https://doi.org/10.1145/31336.31338

Botev A, Lever G, Barber D. Nesterov's accelerated gradient and momentum as approximations to regularised update descent. 2017 International Joint Conference on Neural Networks (IJCNN): IEEE, 2017: p. 1899-1903.

https://doi.org/10.1109/IJCNN.2017.7966082 DOI: https://doi.org/10.1109/IJCNN.2017.7966082

Bottou L. Large-scale machine learning with stochastic gradient descent. In: Lechevallier Y, Saporta G, editors. Proceedings of COMPSTAT'2010. Heidelberg: Physica-Verlag HD; 2010: p. 177-186.

https://doi.org/10.1007/978-3-7908-2604-3_16 DOI: https://doi.org/10.1007/978-3-7908-2604-3_16

Willmott CJ, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res 2005; 30: 79-82.

https://doi.org/10.3354/cr030079 DOI: https://doi.org/10.3354/cr030079

Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng 2018; 2: 719-731.

https://doi.org/10.1038/s41551-018-0305-z DOI: https://doi.org/10.1038/s41551-018-0305-z

Shen D, Wu G, Suk HI. Deep Learning in medical image analysis. Annu Rev Biomed Eng 2017; 19: 221-248.

https://doi.org/10.1146/annurev-bioeng-071516-044442 DOI: https://doi.org/10.1146/annurev-bioeng-071516-044442

Urban G, Feil N, Csuka E, Hashemi K, Ekelem C, Choi F, et al. Combining deep learning with optical coherence tomography imaging to determine scalp hair and follicle counts. Lasers Surg Med 2021; 53: 171-178.

https://doi.org/10.1002/lsm.23324 DOI: https://doi.org/10.1002/lsm.23324

Lee S, Lee JW, Choe SJ, Yang S, Koh SB, Ahn YS, et al. Clinically applicable deep learning framework for measurement of the extent of hair loss in patients with alopecia areata. JAMA Dermatol 2020; 156: 1018-1020.

https://doi.org/10.1001/jamadermatol.2020.2188 DOI: https://doi.org/10.1001/jamadermatol.2020.2188

Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al. Ssd: Single shot multibox detector. European conference on computer vision: Springer, 2016: p. 21-37.

https://doi.org/10.1007/978-3-319-46448-0_2 DOI: https://doi.org/10.1007/978-3-319-46448-0_2

Rushton DH, Ramsay ID, Norris MJ, Gilkes JJ. Natural progression of male pattern baldness in young men. Clin Exp Dermatol 1991; 16: 188-192.

https://doi.org/10.1111/j.1365-2230.1991.tb00343.x DOI: https://doi.org/10.1111/j.1365-2230.1991.tb00343.x

Whiting DA. Diagnostic and predictive value of horizontal sections of scalp biopsy specimens in male pattern androgenetic alopecia. J Am Acad Dermatol 1993; 28: 755-763.

https://doi.org/10.1016/0190-9622(93)70106-4 DOI: https://doi.org/10.1016/0190-9622(93)70106-4

Downloads

Additional Files

Published

2022-01-26

How to Cite

Gao, M., Wang, Y., Xu, H., Xu, C., Yang, X., Nie, J., Zhang, Z., Li, Z., Hou, W., & Jiang, Y. (2022). Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia. Acta Dermato-Venereologica, 102, adv00635. https://doi.org/10.2340/actadv.v101.564