Optimization of the automated Sunnybrook Facial Grading System - Improving the reliability of a deep learning network with facial landmarks.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-08-07 DOI:10.1016/j.anorl.2024.07.005

T C Ten Harkel, F Bielevelt, H A M Marres, K J A O Ingels, T J J Maal, C M Speksnijder

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引用次数: 0

Abstract

Objective: The Sunnybrook Facial Grading System (SFGS) is a well-established grading system to assess the severity and progression of a unilateral facial palsy. The automation of the SFGS makes the SFGS more accessible for researchers, students, clinicians in training, or other untrained co-workers and could be implemented in an eHealth environment. This study investigated the impact on the reliability of the automated SFGS by adding a facial landmark layer in a previously developed convolutional neural network (CNN).

Methods: An existing dataset of 116 patients with a unilateral peripheral facial palsy and 9 healthy subjects performing the SFGS poses was used to train a CNN with a newly added facial landmark layer. A separate model was trained for each of the 13 elements of the SFGS and then used to calculate the SFGS subscores and composite score. The intra-class coefficient of the automated grading system was calculated based on three clinicians experienced in the grading of facial palsy.

Results: The inter-rater reliability of the CNN with the additional facial landmarks increased in performance for all composite scores compared to the previous model. The intra-class coefficient for the composite SFGS score increased from 0.87 to 0.91, the resting symmetry subscore increased from 0.45 to 0.62, the symmetry of voluntary movement subscore increased from 0.89 to 0.92, and the synkinesis subscore increased from 0.75 to 0.78.

Conclusion: The integration of a facial landmark layer into the CNN significantly improved the reliability of the automated SFGS, reaching a performance level comparable to human observers. These results were attained without increasing the dataset underscoring the impact of incorporating facial landmarks into a CNN. These findings indicate that the automated SFGS with facial landmarks is a reliable tool for assessing patients with a unilateral peripheral facial palsy and is applicable in an eHealth environment.

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优化桑尼布鲁克面部自动分级系统--利用面部地标提高深度学习网络的可靠性。

目的：桑尼布鲁克面瘫分级系统（SFGS）是一套成熟的分级系统，用于评估单侧面瘫的严重程度和进展情况。SFGS 的自动化使研究人员、学生、接受培训的临床医生或其他未经培训的同事更容易使用 SFGS，并可在电子健康环境中实施。本研究调查了在先前开发的卷积神经网络（CNN）中添加面部地标层对自动 SFGS 可靠性的影响：方法：使用一个包含 116 名单侧周围性面瘫患者和 9 名健康受试者的现有数据集来训练带有新添加的面部地标层的 CNN。针对 SFGS 的 13 个元素分别训练了一个单独的模型，然后用于计算 SFGS 的子分数和综合分数。根据三位在面瘫分级方面经验丰富的临床医生计算出了自动分级系统的类内系数：结果：与之前的模型相比，增加了面部地标的 CNN 在所有综合评分方面的评分者间可靠性都有所提高。SFGS 综合评分的类内系数从 0.87 增加到 0.91，静息对称性子评分从 0.45 增加到 0.62，自主运动对称性子评分从 0.89 增加到 0.92，同步运动子评分从 0.75 增加到 0.78：将面部地标层整合到 CNN 中可显著提高自动 SFGS 的可靠性，达到与人类观察者相当的性能水平。这些结果是在不增加数据集的情况下取得的，凸显了将面部地标纳入 CNN 的影响。这些研究结果表明，带有面部地标的自动 SFGS 是评估单侧周围性面瘫患者的可靠工具，适用于电子健康环境。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.