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- 저자 : Espaillat, Catherine (검색결과 2 건)
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Artificial-intelligence-automated machine learning as a tool for evaluating facial rhytid images
Alejandro Espaillat 대한미용의학회 2023 대한미용의학회지 Vol.7 No.2
Background: The growing demand for nonsurgical cosmetic treatments necessitates a reliable diagnostic tool to assess the extent of aging, severity of facial wrinkles, and effectiveness of minimally invasive aesthetic procedures. This is crucial to accurately predict the need for botulinum neurotoxin type A neuromodulator injections during facial aesthetic rejuvenation. Objective: This study aimed to determine the accuracy of artificial intelligence-based machine learning algorithms in analyzing facial rhytid images during facial aesthetic evaluation. Methods: A prospective validation model was implemented using a dataset of 3,000 de-identified facial rhytid images from 600 patients in a community private medical spa aesthetic screening program. A neural architecture based on Google Cloud’s artificial intelligence-automated machine learning was developed to detect dynamic hyperkinetic skin lines in various facial muscles. Images were captured using a handheld iPad camera and labeled by an American board-certified ophthalmologist using established quantitative grading scales. The dataset was divided into training (80%), validation (10%), and testing (10%) sets. The model’s performance was evaluated using the following metrics: area under the precision-recall curve, sensitivity, specificity, precision, and accuracy. Results: Facial rhytid images were detected in 79.9%, 10.7%, and 9.3% of the training sets, respectively. The model achieved an area under the precision-recall curve of 0.943, with an accuracy of 91.667% and a recall of 81.881% at a threshold score of 0.5. Conclusion: This study demonstrates the successful application of artificial-intelligence-automated machine learning in identifying facial rhytid images captured using simple photographic devices in a community-based private medical spa program. Thus, the potential value of machine-learning algorithms for evaluating the need for minimally invasive injectable procedures for facial aesthetic rejuvenation was established.
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Kim, K. H.,Watson, Dan M.,Manoj, P.,Forrest, W. J.,Furlan, Elise,Najita, Joan,Sargent, Benjamin,Herná,ndez, Jesú,s,Calvet, Nuria,Adame, Lucí,a,Espaillat, Catherine,Megeath, S. T.,Muz American Astronomical Society 2016 The Astrophysical journal Supplement series Vol.226 No.1
<P>We present our investigation of 319 Class II objects in Orion A observed by Spitzer/IRS. We also present the follow-up observations of 120 of these Class II objects in Orion A from the Infrared Telescope Facility/SpeX. We measure continuum spectral indices, equivalent widths, and integrated fluxes that pertain to disk structure and dust composition from IRS spectra of Class II objects in Orion A. We estimate mass accretion rates using hydrogen recombination lines in the SpeX spectra of our targets. Utilizing these properties, we compare the distributions of the disk and dust properties of Orion A disks with those of Taurus disks with respect to position within Orion A (Orion Nebular Cluster [ONC] and L1641) and with the subgroups by the inferred radial structures, such as transitional disks (TDs) versus radially continuous full disks (FDs). Our main findings are as follows. (1) Inner disks evolve faster than the outer disks. (2) The mass accretion rates of TDs and those of radially continuous FDs are statistically significantly displaced from each other. The median mass accretion rate of radially continuous disks in the ONC and L1641 is not very different from that in Taurus. (3) Less grain processing has occurred in the disks in the ONC compared to those in Taurus, based on analysis of the shape index of the 10 mu m silicate feature (F-11.3/F-9.8). (4) The 20-31 mu m continuum spectral index tracks the projected distance from the most luminous Trapezium star, theta(1) Ori C. A possible explanation is UV ablation of the outer parts of disks.</P>
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