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.

285 Aspirin interrupts bile duct carcinoma in rats induced by thioacetamide

根據(jù)車輛在會車過程中的響應曲線可知，在幾項安全性指標中，輪軌垂向力與輪重減載率在會車過程中有較大的安全余量；而輪軸橫向力和脫軌系數(shù)在450 km/h工況下會在短暫的時間中超過安全限值。這是由于會車氣動流場對車體的橫向作用力較大，主要影響與輪軌橫向力有關的安全性指標。通過觀察輪軸橫向力和脫軌系數(shù)超過安全限值的峰值點可知，運行安全性指標的危險點一般出現(xiàn)在交會列車前部鼻端通過觀測點的時刻，故應在高速列車的鼻端設計中設法降低會車時的初始壓力波幅度，以提高動車組在高速會車時的運行安全性。

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 “l(fā)oss 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.

當然，更重要的是，我們要從文化素養(yǎng)和道德建設的層面，深刻反思中華民族優(yōu)秀文化傳統(tǒng)所出現(xiàn)的嚴重斷層，華夏千年禮儀之邦，如今竟至斯文掃地，四處丟丑，乃至遭人蔑視。說到底，如今整個社會道德水準亟待提高，造成這種現(xiàn)狀的原因很復雜，全社會都有責任。二十年前，我曾與著名社會學家金耀基教授進行過一次有關中華文化的對話，他的一句名言令我至今難忘：“二十世紀初的中國人曾經(jīng)看不起中華文化，然而一路掃蕩下來，到了二十世紀末，中國人已經(jīng)看不見中華文化了！”這是多么痛徹而嚴酷的現(xiàn)實啊！

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學特性研究…………………………………………… 趙永強，徐 振，楊冬靜，孫厚俊，謝逸萍，張成玲（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).

從表1看出，冬季分蘗數(shù)以鄭麥1860的最高，為44.2萬穗/畝，其次是輪選166，為37.8萬穗/畝，泰禾麥2號的最低，僅為27.1萬穗/畝；春季分蘗以輪選166和泉麥29的較高，分別為98.9萬穗/畝和98.4萬穗/畝，其余依次是鄭麥1860>珍麥3號>周麥18>泰禾麥2號>農大2011，農大2011的最低，為77.6萬穗/畝；株高在66～76厘米,以鄭麥1860的最高，泉麥29的最低；各品種生育期在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.

（4）A:We’ve decided to enlarge the production as there is a strongdemand fromoverseas.

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.絕對禁止說。該學說認為監(jiān)聽具有隱秘性，其對隱私權的侵害以及濫用的可能性比搜查嚴重，對于偶然監(jiān)聽所獲得的另案證據(jù)，由于其并非原來調查的罪名，不符合監(jiān)聽的要件，所以不論其是否屬于德國刑事訴訟法第100條(a)所列的可得監(jiān)聽的罪名，也不論對被告人還是第三人，均不得作為證據(jù)使用。目前該學說為少數(shù)說[1]。

①為確保工程項目劃分的合理性，必須堅持項目劃分確認程序。一些工程在制定項目劃分時，施工單位先劃分，經(jīng)監(jiān)理審核后，由項目法人報監(jiān)督機構確認，這樣易造成項目劃分不合理。主要表現(xiàn)：一是存在以施工單位為主導進行項目劃分的問題，缺少項目法人與施工單位及監(jiān)督機構與監(jiān)理、施工單位的溝通環(huán)節(jié)；二是設計單位沒有按要求參與到項目劃分中，特別是在開工初期，各單位對施工圖還沒有完全吃準摸透，項目劃分易缺項漏項。

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，我國政府部門還需積極進行行政體制的改革工作，對各級政府的事權進行明確規(guī)定，理清各個部門在國土管控方面的職責。在國家層面上，已確定由同一個部門進行空間綜合規(guī)劃編制、國土資源空間布局與管控等工作的執(zhí)行，并要求國家對地方的發(fā)展、土地的使用進行宏觀的管控干預，借此來改變以往行政管理為主的局面，從而實現(xiàn)簡政放權。此外還需相關部門根據(jù)空間規(guī)劃體系的具體架構，構建出完善的法律法規(guī)制度，并對我國現(xiàn)階段的《土地管理法》、《城鄉(xiāng)規(guī)劃法》等進行系統(tǒng)的優(yōu)化與完善，使得各級政府部門的管理職能得到充分的發(fā)揮。

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

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