Facial Scanning With a Digital Camera: A Novel Way of Screening for Primary Angle Closure

Comparison of a custom Photogrammetry for Anatomical CarE (PHACE) system with other Low- Cost Facial Scanning Devices

Purpose

To compare a custom Photogrammetry for Anatomical CarE (PHACE) system with other cost-effective 3-dimensional (3D) facial scanning systems to objectively characterize morphology and volume of periorbital and adnexal anatomy.

Methods

The imaging systems evaluated include the low-cost custom PHACE system and commercial software product for the iPhone called Scandy Pro (iScandy) application (Scandy, USA), and the mid-priced Einscan Pro 2X (Shining3D Technologies, China) device and Array of Reconstructed Cameras 7 (ARC7) facial scanner (Bellus3D, USA). Imaging was performed on a manikin facemask and humans with various Fitzpatrick scores. Scanner attributes were assessed using mesh density, reproducibility, surface deviation, and emulation of 3D printed phantom lesions affixed above the superciliary arch (brow line).

Results

The Einscan served as a reference for lower cost imaging systems because it qualitatively and quantitatively renders facial morphology with the highest mesh density, reproducibility (0.13 ± 0.10 mm), and volume recapitulation (approximately 2% of 33.5 μL). Compared to the Einscan, the PHACE system (0.35 ± 0.03 mm, 0.33 ± 0.16 mm) demonstrated non-inferior mean accuracy and reproducibility root mean square (RMS) compared to the iScandy (0.42 ± 0.13 mm, 0.58 ± 0.09 mm), and significantly more expensive ARC7 (0.42 ± 0.03 mm, 0.26 ± 0.09 mm). Similarly, the PHACE system showed non-inferior volumetric modeling when rendering a 124 μL phantom lesion compared to the iScandy and more costly ARC7 (mean percent difference from the Einscan: 4.68 ± 3.73%, 9.09 ± 0.94%, and 21.99 ± 17.91% respectively).

Conclusions

The affordable PHACE system accurately measures periorbital soft tissue as well as other established mid-cost facial scanning systems. Additionally, the portability, affordability, and adaptability of PHACE can facilitate widespread adoption of 3D facial anthropometric technology as an objective measurement tool in ophthalmology.

Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

With the aim of improving the shortcomings of the traditional single hidden layer back propagation (BP) neural network structure and learning algorithm, this paper proposes a centrifugal pump performance prediction method based on the combination of the Levenberg–Marquardt (LM) training algorithm and double hidden layer BP neural network. MATLAB was used to establish a double hidden layer BP neural network prediction model to predict the head and efficiency of a centrifugal pump. The average relative error of the head between the experimental and prediction obtained by the double hidden layer BP neural network model was 4.35%, the average relative error of the model prediction efficiency and the experimental efficiency was 2.94%, and the convergence time was 1/42 of that of the single hidden layer. The double hidden layer BP neural network model effectively solves the problems of low learning efficiency and easy convergence into local minima—issues that were common in the traditional single hidden layer BP neural network training. Furthermore, the proposed model realizes hydraulic performance prediction during the design process of a centrifugal pump.