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      Objective and automatic grading system of facial signs from smartphones’ pictures in South African men: Validation versus dermatologists and characterization of changes with age

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          Abstract

          Objective

          To evaluate the capacity of the automatic detection system to accurately grade, from selfie pictures, the severity of eight facial signs in South African men.

          Methods

          Selfie pictures (obtained from frontal and back cameras) of 281 South African men differently aged (20–70 years) were obtained and analyzed by an automatic artificial intelligence (AI)‐based automatic grading system. Data were compared with the clinical gradings made by experts and dermatologists.

          Results

          In all facial signs, both series of gradings were found highly correlated with, however, different coefficients (0.59–0.95), those of marionette lines and cheek pores being of lower values. No differences were observed between data obtained by frontal and back cameras. With age, in most cases, gradings show up to the 50–59 year age‐class, linear‐like changes. When compared to men of other ancestries, South African men present lower wrinkles/texture, pigmentation, and ptosis/sagging scores till 50–59 years, albeit not much different in the cheek pores sign. The early onset (mean age) of visibility of wrinkles/texture for South African men were (i.e., reaching grade >1) 39 and 45 years for ptosis/sagging.

          Conclusion

          This study completes and enlarges the previous works conducted on men of other ancestries by showing some South African specificities and slight differences with men of comparable phototypes (Afro American).

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          Most cited references52

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          A deep learning system for differential diagnosis of skin diseases

          Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.
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            Epidermal thickness at different body sites: relationship to age, gender, pigmentation, blood content, skin type and smoking habits.

            Epidermal thickness and its relationship to age, gender, skin type, pigmentation, blood content, smoking habits and body site is important in dermatologic research and was investigated in this study. Biopsies from three different body sites of 71 human volunteers were obtained, and thickness of the stratum corneum and cellular epidermis was measured microscopically using a preparation technique preventing tissue damage. Multiple regressions analysis was used to evaluate the effect of the various factors independently of each other. Mean (SD) thickness of the stratum corneum was 18.3 (4.9) microm at the dorsal aspect of the forearm, 11.0 (2.2) microm at the shoulder and 14.9 (3.4) microm at the buttock. Corresponding values for the cellular epidermis were 56.6 (11.5) microm, 70.3 (13.6) microm and 81.5 (15.7) microm, respectively. Body site largely explains the variation in epidermal thickness, but also a significant individual variation was observed. Thickness of the stratum corneum correlated positively to pigmentation (p = 0.0008) and negatively to the number of years of smoking (p < 0.0001). Thickness of the cellular epidermis correlated positively to blood content (P = 0.028) and was greater in males than in females (P < 0.0001). Epidermal thickness was not correlated to age or skin type.
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              MobileNetV2: Inverted Residuals and Linear Bottlenecks

              In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters
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                Author and article information

                Contributors
                frederic.flament@rd.loreal.com
                Journal
                Skin Res Technol
                Skin Res Technol
                10.1111/(ISSN)1600-0846
                SRT
                Skin Research and Technology
                John Wiley and Sons Inc. (Hoboken )
                0909-752X
                1600-0846
                27 April 2023
                April 2023
                : 29
                : 4 ( doiID: 10.1111/srt.v29.4 )
                : e13257
                Affiliations
                [ 1 ] L'Oréal Research and Innovation Clichy France
                [ 2 ] ModiFace ‐ A L'Oréal Group Company Toronto Ontario Canada
                [ 3 ] L'Oréal Clichy France
                [ 4 ] Department of Dermatology, Université Côte d'Azur CHU Nice Nice France
                [ 5 ] Université Côte d'Azur INSERM, U1065, C3M Nice France
                Author notes
                [*] [* ] Correspondence

                Frederic Flament, L'Oreal Research and Innovation, 9 Rue Pierre Dreyfus, 92110, Clichy, France.

                Email: frederic.flament@ 123456rd.loreal.com

                Article
                SRT13257
                10.1111/srt.13257
                10234158
                37113093
                8e06f5b5-a386-468a-9e5a-80ec89a476f5
                © 2022 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 25 July 2022
                : 02 December 2022
                Page count
                Figures: 5, Tables: 3, Pages: 9, Words: 4468
                Funding
                Funded by: L'Oréal Research & Innovation
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                April 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.8 mode:remove_FC converted:01.06.2023

                artificial intelligence,clinical signs,diversity,men,nomad evaluation

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