Artificial intelligence improves accuracy, efficiency, and reliability of a handheld infrared eccentric autorefractor for adult refractometry

INTRODUCTION

Ametropia is now a serious public health concern worldwide. Globally, it was estimated that there were 312 million cases of myopia in 2015. Nearly 5 billion people will be affected by 2050. A higher incidence of myopia means more pathological myopia patients. Refractive error has become one of the leading causes of visual impairment and preventable blindness among children and young adults.

Based on the above, regular and large-scale vision screening should be implemented as soon as possible. Accurate, affordable,and portable measuring equipment is needed to screen large populations. Retinoscopy, table-mounted autorefractors (TAR),and handheld automatic refractors are often used for vision screening. Retinoscopy, which estimates refractive power by measuring the divergence of reflected light, requires experienced and skilled optometrists. TAR is widely used and technological innovations have improved their precision.However, measuring visual acuity in subjects who are older or very young, or in those that have a disability may be more challenging; consequently, portable handheld autorefractors are also frequently used to measure visual acuity.

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根据车辆在会车过程中的响应曲线可知，在几项安全性指标中，轮轨垂向力与轮重减载率在会车过程中有较大的安全余量；而轮轴横向力和脱轨系数在450 km/h工况下会在短暂的时间中超过安全限值。这是由于会车气动流场对车体的横向作用力较大，主要影响与轮轨横向力有关的安全性指标。通过观察轮轴横向力和脱轨系数超过安全限值的峰值点可知，运行安全性指标的危险点一般出现在交会列车前部鼻端通过观测点的时刻，故应在高速列车的鼻端设计中设法降低会车时的初始压力波幅度，以提高动车组在高速会车时的运行安全性。

This research investigated whether AI improved the clinical utility of hICA by comparing the values of diopter measurement and time control, and provides insight that could aid the development of accurate and efficient autorefractors.

SUBJECTS AND METHODS

The study adhered to the tenets of the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of Shanghai Ninth People's Hospital,affiliated with Shanghai Jiao Tong University School of Medicine (Shanghai, China; SH9H-2020-T22-2). The study objectives and procedures were explained to all subjects in advance, and written informed consent was obtained.

Subjects with small pupils (bilateral pupil diameter＜2 mm in indoor light) and ocular diseases were excluded from the study. In total, 70 healthy adult volunteers participated.Subjects with a visual acuity ＜20/20 with correction in one eye were not eligible to participate. Data on age, date of birth, sex,spectacle use, and ophthalmological findings were collected.

Three instruments were tested in this study,namely an automatic refractor (AR-1; Nidek, Gamagori, Japan)and two automatic vision screeners: the VS100 Spot Vision Screener (Welch Allyn, Skaneateles Falls, NY, USA) and the V100 Vision Screener (MediWorks, Shanghai, China).The appropriate rights to reproduce or mentioned of the V100 Vision Screener has been obtained from Shanghai MediWorks Precision Instruments Company Limited. All three instruments were calibrated before testing.

The AI binocular measurement method described here is based on deep learning.

The U-net segmentation network described by Ronnebergerin 2015 is widely used for medical image segmentation. U-net were used to segment the pupil area from red/green/blue (RGB) images.The image resolution was 320×240, and probability maps were generated by convolution, skip connection, and deconvolution operations. The pupil area was considered to correspond to the probability map that exceeded the probability threshold(Figure 1). The U-net neural network enhances information,decreases the loss thereof, and greatly improves the accuracy of medical images. As shown in Figure 2, the network framework includes an encoder, decoder, and skip connection.The encoder extracts image features, such as shallow layers and fine granular structures. The decoder restores the features,including shallow- and deep-channel features, and converts image information from low to high resolution. The decoding module can express deep- and coarse-grained features. Next,the ROI is located using probability maps. The skip connection links the encoder and the decoder, reduces information loss during the feature extraction process, and ensures accurate positioning and segmentation.

A total of 20 000 human eye images were collected and separated into a training set and a verification set (ratio of 4:1). Data augmentation was applied, including rotation, translation,scaling, grey-level stretching, and randomisation. Then the images were normalised by subtraction and accommodating variation. The “loss cross-entropy function” was dichotomous,with “0” representing the background and “1” representing the pupil. The “U-net training weight” was used as the initial weight before fine-tuning the training dataset. Stochastic gradient-descent with an optimised iteration method was applied for 60 rounds. The initial learning rate of 0.01 decreased 10-fold after 20 rounds, and then again after 40 rounds. Finally,the training weight with the minimum difference between the training and verification set data loss was selected for network reasoning. The U-net network inference procedure generated probability maps with thresholds. Areas with a probability ＞0.8 were designated as pupillary regions; the remaining areas were considered background. Next, a binary mask for the pupillary region was obtained and used to extract the pupillary ROI from the original image. Then the infrared eccentricity algorithm was used to obtain diopter values.

当然，更重要的是，我们要从文化素养和道德建设的层面，深刻反思中华民族优秀文化传统所出现的严重断层，华夏千年礼仪之邦，如今竟至斯文扫地，四处丢丑，乃至遭人蔑视。说到底，如今整个社会道德水准亟待提高，造成这种现状的原因很复杂，全社会都有责任。二十年前，我曾与著名社会学家金耀基教授进行过一次有关中华文化的对话，他的一句名言令我至今难忘：“二十世纪初的中国人曾经看不起中华文化，然而一路扫荡下来，到了二十世纪末，中国人已经看不见中华文化了！”这是多么痛彻而严酷的现实啊！

In the first and second steps, two professional optometrists simultaneously obtained the measurements for each subject;each optometrist used a different vision screener. Then the optometrists swapped the vision screeners before the next round of measurements. Therefore, all subjects were evaluated using both vision screeners, and by both optometrists, under bright and intense light conditions. Measurements that took more than 20s were considered failures. The Welch Allyn VS100 and MediWorks V100 devices were positioned approximately 1 m from the face of each participant to obtain the measurements.

To evaluate the efficiency of each vision screener, measurement times were recorded for each subject by two timekeepers with two stopwatches of the same type (from the point at which the binocular image appeared on the screen until the results were outputted).

甘薯黑痣病菌的生物学特性研究…………………………………………… 赵永强，徐 振，杨冬静，孙厚俊，谢逸萍，张成玲（89）

Parameters for Refractive Error Measurements Measurements recorded using the TAR were used as the reference standard. The diopter of spherical power (DS)and cylindrical power (DC) were decomposed into vertical/horizontal component (J0=-(DC/2)×cos(2A), A means axis)and oblique component [J45=-(DC/2)×sin(2A)] of refractive,and spherical equivalent (SE; the DS plus half of the negative DC) were used to evaluate the accuracy of both of the handheld infrared eccentric autorefractors used in this study.

RESULTS

In total, 140 eyes of 70 participants were assessed. The sociodemographic characteristics of the participants are shown in Table 1.

Statistical Analysis The data collected during the project were processed using Excel software (Microsoft Corp.,Redmond, WA, USA). Next, the data were reviewed for errors and analysed using SPSS software (ver. 24.0; IBM Corp.,Armonk, NY, USA). The normality of the distribution of the optometry data was assessed using the Shapiro-Wilk test. For qualitative data, frequencies and proportions were calculated.Descriptive statistics were generated for the quantitative data,as medians and interquartile ranges (IQRs), because these data were not normally distributed. To avoid analytical difficulties associated with the interdependence of observations between eyes from the same individual, a generalised equation was used to compare the SE, DS, and DC measurements, and the times thereof, among the different groups. The intraclass correlation coefficient (ICC) and Spearman's rank correlation coefficient were used to evaluate correlations among the measurements recorded by the three instruments. Bland-Altman were used to analyze the precision of the equipment by the agreement.The tests were two-sided, and a-value ＜0.05 was considered statistically significant.

Handheld automatic refractors are convenient to use, and many studies have compared their accuracy and efficiency with traditional clinical optometry methods. Results have shown that measurements of astigmatism, myopia, and anisometropia recorded using these handheld autorefractors are consistent with those recorded using cycloplegic retinoscopy.However, these refractors are associated with small errors and may be affected by external factors. The measurement of refractive error using a handheld infrared eccentric autorefractor(hICA) is based on light tracing, which may be affected by changes in light intensity, humidity, movement caused by hand-shake, focusing blur, or eye deformation. Deep learning,as a neoteric form of artificial intelligence (AI), could improve the stability and robustness of these procedures by enhancing the representativeness of data in the form of text, images, or sound. In this study, AI was applied to increase the accuracy of hICA measurements obtained during vision screening.

In a brightly lit environment (161.2 lx), the median (IQR) SE values measured using the MediWorks V100, Welch Allyn VS100, and Nidek AR-1 instruments were -1.250 (2.47) D, -1.187 (2.973) D,and -1.678 (3.094) D, respectively. There were no significant differences in the estimated marginal mean SE, DS, and DC values (J0 and J45) obtained using the Welch Allyn VS100 and Nidek AR-1 (＞0.05). The estimated marginal mean SE, DS,and DC (J0, J45) values obtained using the three instruments are presented in Table 2.

Of the two hICAs, the instrument equipped with AI (MediWorks V100) showed the better detection rate(100%70% in an intense-light environment).

In total, 98 eyes of 49/70 (70%) participants were successfully evaluated using the Welch Allyn VS100. Therefore, the SE measurements of these 49 subjects were analysed. In an intense-light environment(1043 lx), the medians (IQR) SE values measured using the MediWorks V100, Welch Allyn VS100, and Nidek AR-1 instruments were -1.303 (2.89) D, -1.522 (3.164) D, and-2.030 (3.124) D, respectively. Similar to the results obtained in the brightly lit environment, the DC values significantly differed between MediWorks V100 and Nidek AR-1 (＜0.05).There were statistically significant differences in the SE and DS values obtained using the Welch Allyn VS100 and Nidek AR-1 instruments (＜0.05). The estimated marginal mean SE,DS, J0 and J45 values obtained using the three instruments are presented in Table 3.

In an intense-light environment (1043 lx), the ICC for the SE between the MediWorks V100 and Nidek AR-1 instruments was 0.956 (＜0.001), and that between the Welch Allyn VS100 and Nidek AR-1 instruments was 0.973 (＜0.001). The ICC and Bland-Altman analyses indicated a high degree of consistency and repeatability for the SE and DS measurements obtained using the two vision screeners and the TAR.

Light intensity had a significant effect on the dioptric measurements recorded using both handheld screeners (＜0.05), whereas it had little effect on the TAR measurements (＞0.05; Table 3).

从表1看出，冬季分蘖数以郑麦1860的最高，为44.2万穗/亩，其次是轮选166，为37.8万穗/亩，泰禾麦2号的最低，仅为27.1万穗/亩；春季分蘖以轮选166和泉麦29的较高，分别为98.9万穗/亩和98.4万穗/亩，其余依次是郑麦1860>珍麦3号>周麦18>泰禾麦2号>农大2011，农大2011的最低，为77.6万穗/亩；株高在66～76厘米,以郑麦1860的最高，泉麦29的最低；各品种生育期在245～248天，相差不大。

In a brightly lit environment (161.2 lx), the ICC for the SE between the MediWorks V100 and Nidek AR-1 instruments was 0.925 (＜0.001), and that between the Welch Allyn VS100 and Nidek AR-1 was 0.955 (＜0.001). There was a statistically significant correlation in the SE, DS and DC measurements recorded using both vision screeners and the TAR (＜0.05).

As shown in Table 4, the estimated marginal mean length of time necessary to record measurements in both the brightly lit (=0.008) and an intense-light (=0.002)environments was shorter when using the MediWorks V100 than when using the Welch Allyn VS100. Lower light intensity decreased the time necessary for both screeners to complete the dioptric measurements in both environments.

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DISCUSSION

Recent studies have evaluated the performance of deep learning-based algorithms for diagnosing ophthalmic diseasesimage analyses. This study describes a theoretical and experimental approach to vision screening using AI technology.In this cross-sectional study, the mean dioptric measurement values and times were compared between two hICAs, the MediWorks V100 and Welch Allyn VS100 instruments, and a TAR, Nidek AR-1.

The results indicated that AI could play an important role in challenging vision screening environments. In a brightly lit environment, the SE and DS measurements obtained using thehICA without AI were less negative than those obtained using TAR (＞0.05). Similar findings have been reported in previous studies. In the intense-light environment in this study,the SE and DS values obtained using the hICA equipped with AI were more similar to the reference standard values. One explanation for the higher detection rate (100%70%) and more rapid measurements observed using the vision screener with AI under intense light (＜0.05) is that AI overcomes some of the disadvantages associated with traditional image processing and enhances the sensitivity and robustness of the instrument through more precise detection and recognition in complex environments. There was statistically significant agreement in the SE and DS measurements obtained using the hICA and the TAR, which suggests that these vision screeners may be suitable for large-scale clinical screening and evaluation of patients who cannot be assessed using conventional refractometry.

Photo-screening technology is increasingly being used for optical screening due to its numerous advantages, such as high-speed binocular measurements, minimal training requirements, and a compact and lightweight instrument design. This is the first study to combine AI and photoscreening technology to assess the accuracy and efficiency of these instruments when used in healthy adults. One recent study applied deep learning for myopia screening of children and achieved high screening accuracy using deep convolution neural networks, thus demonstrating the potential benefits of AI for vision screening. Deep learning was proven to be effective for estimating refractive error in clinical practice. AI may be applied to improve routine, large-scale screening for myopia.

4.绝对禁止说。该学说认为监听具有隐秘性，其对隐私权的侵害以及滥用的可能性比搜查严重，对于偶然监听所获得的另案证据，由于其并非原来调查的罪名，不符合监听的要件，所以不论其是否属于德国刑事诉讼法第100条(a)所列的可得监听的罪名，也不论对被告人还是第三人，均不得作为证据使用。目前该学说为少数说[1]。

①为确保工程项目划分的合理性，必须坚持项目划分确认程序。一些工程在制定项目划分时，施工单位先划分，经监理审核后，由项目法人报监督机构确认，这样易造成项目划分不合理。主要表现：一是存在以施工单位为主导进行项目划分的问题，缺少项目法人与施工单位及监督机构与监理、施工单位的沟通环节；二是设计单位没有按要求参与到项目划分中，特别是在开工初期，各单位对施工图还没有完全吃准摸透，项目划分易缺项漏项。

This study did have some limitations. In particular, although auto-refractometry is now established as a reliable tool for measuring refractive error and visual acuity, the manual refraction after cycloplegia remains as the gold standard but was not used in the study. Previous studies have shown that different autorefractors produce significantly different SE measurements, using both objective and subjective refraction. Thus, further studies are needed to compare the accuracy of automatic refractors equipped with AI and subjective refractors, with and without cycloplegia. Handheld automatic refractors are particularly suitable for assessing vision in infants, preschool children, older subjects with mobility difficulties, and those at risk for amblyopia or severe refractive defects. Further studies are needed to better understand the typical values in various populations. DC measurements recorded using handheld automatic refractors equipped with AI were not particularly accurate. However, this inaccuracy was eliminated after decomposing DC into J0 and J45 and analyzing separately. There are still several uncertain factors in the study. First, binocular accommodation varies significantly among individuals. Second, hICA and TAR are based on different principles. Measurement distances, algorithms, and calibration criteria may vary significantly between the two instruments. Third, the results in this study may have been affected by various other factors such as measurement distance,light, humidity, eye movements, and a small sample size; these factors could explain why the DC measurement results differed from those recorded in previous studies.

In conclusion, this study tested the effectiveness of an AIenabled hICA for clinical vision screening and found that the AI technology improved the accuracy and speed of measurements in complex environments for normal human eyes without diseases. Future research efforts should be directed toward large-scale screening and early detection/prevention of myopia.

除了上述措施外，我国政府部门还需积极进行行政体制的改革工作，对各级政府的事权进行明确规定，理清各个部门在国土管控方面的职责。在国家层面上，已确定由同一个部门进行空间综合规划编制、国土资源空间布局与管控等工作的执行，并要求国家对地方的发展、土地的使用进行宏观的管控干预，借此来改变以往行政管理为主的局面，从而实现简政放权。此外还需相关部门根据空间规划体系的具体架构，构建出完善的法律法规制度，并对我国现阶段的《土地管理法》、《城乡规划法》等进行系统的优化与完善，使得各级政府部门的管理职能得到充分的发挥。

Supported by the Science and Technology Commission of Shanghai (No.17DZ2260100).

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