近视是危害儿童青少年视力最常见的眼部疾病，高度近视对视功能造成极大的威胁。近年来，我国近视发病率逐年升高，对近视筛查与防控的需求也不断增加，随着人工智能理论与技术的不断发展与成熟，可以辅助眼科医生进行近视筛查、诊断与治疗。本文将简要介绍人工智能在近视的筛查、预测、检测、病理性近视以及角膜屈光手术中的应用，浅谈了目前人工智能在研究中存在的可比度较低、影像要求较高、可解释性较低及隐私保护等问题，并展望人工智能在近视相关领域的应用前景。
Myopia is the most common ocular disease that harms the vision of children and adolescents. High myopia poses a great threat to visual function. The incidence of myopia in China has been increasing in recent years, and the demand for myopia screening, prevention and control has also expanded. With the continuous development of artificial intelligence theory and technology, Artificial intelligence can assist ophthalmologists in myopia screening, diagnosis and treatment. This review will briefly introduce artificial intelligence in the screening, prediction, and detection of myopia; also, the application in pathological myopia and corneal refractive surgery. This review will discuss some problems of current artificial intelligence research, such as low comparability, high image requirements, low interpretability, privacy protection, and the application prospects of artificial intelligence in myopia.
目的：探索智能语音随访系统在医疗场景中的新型应用服务模式并分析其在新冠肺炎疫情期间的应用效果，以此评估该系统应用于互联网医院开展医疗咨询服务的实际效能。方法：本研究应用智能语音随访系统针对先天性白内障患儿术后的常见问题进行回访。首先，针对随访目的，设计出完善的结构化随访内容与步骤。其次，部署智能外呼系统自动拨打用户电话，并通过语音识别技术对用户的每次应答进行识别，根据用户的应答自动跳转到下一个随访步骤，在完成一系列问答后根据用户的回答给出恰当的建议，实现电话随访的自动化与智能化。收集2020年2月24日至2月28日期间，智能语音随访系统随访的电话内容、呼叫时间、患儿资料等数据，采用描述性统计分析。结果：2020年2月24日至2月28日期间，中山大学中山眼科中心应用智能语音随访系统电话共随访1154例，其中收到有效回访数据561例，平均有效回访率48.6%。有效回访人群中，有204位(36.4%)家属认为疫情期间复诊时间延长，对宝宝眼睛的恢复有影响，309位(55.1%)家属认为对宝宝眼睛的恢复没有影响。360位(64.2%)先天性白内障患儿眼睛恢复情况良好，没有出现不良反应，169位(30.1%)患儿出现不良反应和体征，包括瞳孔区有白点，眼睛发红和有眼屎流眼泪等。统计患儿不同行为显示，有417位(74.3%)患儿佩戴眼镜，135位(24.1%)患儿没有佩戴眼镜，另有9位(1.6%)患儿佩戴眼镜情况不清楚，经常揉眼的患儿更容易出现眼睛发红(20.4%)、眼睛有眼屎或流眼泪(17.0%)和瞳孔区有白点(6.8%)等不良反应。结论：智能语音随访系统在临床随访中显示出巨大的应用潜力，可作为一种新型的智能医疗服务模式。
Objective: This study was designed to explore its potential value for new medical service model based on the intelligent voice follow-up system and analyze its application effect during the outbreak of COVID-19. The actual effectiveness of this intelligent voice follow-up system applied in the Internet hospital to carry out medical consultation service was discussed. Methods: In this study, an intelligent voice follow up system was developed for postoperative follow-up of children with congenital cataract. First, a well-designed and structured questionnaire contents were developed for postoperative follow-up. Secondly, the intelligent voice follow-up system was deployed. The system would automatically jump to the next follow-up step according to the user’s response, and give appropriate suggestions. Finally, the data of telephone recording, call time, children’s attributes were collected and statistically analyzed. Results: From February 24 to March 15, 2020, 561 families of children with congenital cataract from Zhongshan Ophthalmic Center were recruited by using the intelligent voice follow-up system. The system completed a total of 1 154 calls, of which 561 cases received follow-up data, reaching an average effective call rate of 48.6%. Among 561 cases, 204 (36.4%) thought that the extended time of follow-up visit would affect the recovery of children, while 309 (55.1%) thought that it exerted no effect on the recovery. 360 children (64.2%) achieved good ocular recovery without complications, whereas 169 cases (30.1%) developed ocular symptoms. These include white spots in the pupil area, redness and eye secretions. Statistics of different behavior of children showed that there were 417 (74.3%) children wearing glasses, 135 (24.1%) children did not wear glasses, another 9 (1.6%) children wearing glasses were not clear, often rubbing the eyes of children were more likely to appear redness (20.4%), eye secretions (17.0%) and white spots in the pupil area (6.8%) and other adverse reactions. Conclusion: The intelligent voice follow-up system shows great application potential in clinical follow-up, which can be employed as a new service mode of intelligent medical treatment.
目的：评估白内障人工智能辅助诊断系统在社区筛查中的应用效果。方法：采用前瞻性观察性研究方法对白内障人工辅助诊断系统的应用效果进行分析，结合远程医疗的模式，由社区卫生人员对居民进行病史采集、视力检查和裂隙灯眼前节检查等，将数据上传至云平台，由白内障人工智能辅助诊断系统和人类医生依次进行白内障评估。结果：受检人群中男性所占比例为35.7%，年龄中位数为66岁，裂隙灯眼前节照片有98.7%的图像质量合格。该白内障人工智能辅助诊断系统在外部验证集中检出重度白内障的曲线下面积为0.915。在人类医生建议转诊的病例中，有80.3%也由人工智能系统给出了相同的建议。结论：该白内障人工智能辅助诊断系统在白内障社区筛查的应用中具有较好的可行性和准确性，为开展社区筛查疾病提供了参考依据。
Objective: To evaluate the effectiveness of an artificial intelligence-assisted diagnostic system for cataract screening in community. Methods: A prospective observational study was carried out based on a telemedicine platform. Patient history, medical records and anterior ocular segment images were collected and transmitted from community healthcare centers to Zhongshan Ophthalmic Center for evaluation by both ophthalmologists and artificial intelligence-assisted cataract diagnostic system. Results: Of all enumerated subjects, 35.7% were male and the median age was 66 years old. Of all enumerated slit-lamp images, 98.7% met the requirement of acceptable quality. This artificial intelligence-assisted diagnostic system achieved an AUC of 0.915 for detection of severe cataracts in the external validation dataset. For subjects who were advised to be referred to tertiary hospitals by doctors, 80.3% of them received the same suggestion from this artificial intelligence-assisted diagnostic system.Conclusion: This artificial intelligence-assisted cataract diagnostic system showed high applicability and accuracy in community-based cataract screening and could be a potential model of care in community-based disease screening.
眼科人工智能技术在实践中不断发展，如大数据应用、图像信息分析、机器人时代等，现在又迈上促进生物识别精确化的新台阶，这些实践应用都能更好地保护视器官，使之具备正常视功能，展示出独特的视觉信息特色。眼科人工智能技术不断开辟新领域，取得了诸多新成就。
The application of artificial intelligence technology in ophthalmology has been on the agenda, and has continued to progress in practice, such as the application of big data, image information analysis, the era of robotics, and now it is on a new step to promote the accuracy of biometrics. These are protections for the visual organs and the vision, make they have normal visual function, and display unique characteristic of visual information. The “eye and artificial intelligence” has continuously opened up new fields and achieved new successes.
近年来随着人类生活方式的改变、用眼频率的增加，眼科药物的市场需求持续增长，但是目前眼病治疗仍面临“缺医少药”的困境。由于新药研发面临成本高、周期长、成功率低的风险，眼科药物创新迭代的进程日趋缓慢。人工智能(artificial intelligence，AI)作为一种全新的技术手段，有望赋能眼科药物研发的全过程，包括药物靶点发现、化合物筛选、药物动力学模型创新与临床试验开展等，以期为眼科药物研发“降本增效”。且随着大数据体系的完善、硬件计算力的提升以及生命科学与智能科学的深度融合，AI在眼科药物研发中的作用将进一步得到提升，助力眼科药物研发实现从精准化到智能化的跨越。
With the change of human lifestyle and overuse of eyes in recent years, the market demand for ophthalmic drugs continues to grow. However, the ocular therapy is still facing the shortage of doctors and drugs. Due to the risk of high cost, long lead time and low success rate, the process of novel ophthalmic drug innovation and iteration is getting slower. As an emerging technology, artificial intelligence is expected to enable the whole process of ophthalmic drug discovery and development, including drug target discovery, compound screening, pharmacokinetic model innovation and clinical trials, thus reducing R&D costs and increase efficiency for ophthalmic drug discovery and development. In addition, with the improvement of big data, hardware calculation and the deep integration of life science and intelligent science, the role of artificial intelligence in ophthalmic drug discovery and development will be significant improved , contributing to achieve the leap from precision to intelligence.
目的：分析医学人工智能通识课程“眼科人工智能的研发与应用”的开展效果，为相关医学人工智能通识课程的开展提供参考和借鉴。方法：纵向观察性研究。观察分析2020年秋季学期眼科人工智能的研发与应用通识课程学生人群，课程考核结果以及学生对课程的整体评价。结果：共有118名本科生同学参与了课程学习。其中大部分为低年级临床医学专业本科生。期中考核得分为77.21±10.07，有56位同学(47.46%)达到80分以上。期末考核得分为82.24±6.77，有91位同学(77.12%)达到80分以上。同学对课程的评分为98.76±3.55，超过90%的同学表示课程备课认真、授课条理清晰、表达准确。结论：本课程的顺利进展证明医学人工智能联合教学模式的可行性，理论和实践穿插的教学设置帮助同学们更好地掌握知识技术，完成教学目标。
Objective: To analyze the effectiveness of medical education curriculum named “Development and Application of Ophthalmic Artificial Intelligence”, and provide reference for the development of other related curriculums. Methods: Longitudinal observational study method was adopted. During the fall semester of 2020, we conducted an education curriculum named “Development and Application of Ophthalmic Artificial Intelligence” and analyzed the results of mid-term and final examinations, and curriculum evaluation of students. Results: There were 118 undergraduate students taking the course and most of them were junior students majoring in clinical medicine. The score of the mid-term examination was in the range of 77.2±10.07, and 56 students (47.46%) got more than 80 points. The score of the final examination was in the range of 82.24±6.77, and 91 students (77.12%) got more than 80 points. The score of course evaluation of students was in the range of 98.76±3.55, and more than 90% of the students thought that teachers have made full preparations before class, together with clear teaching logic and accurate expressions in class. Conclusion: The smooth progress of our course proved the feasibility of medical artificial intelligence teaching. The teaching setting interspersed with theory and practice could help students to master knowledge and technology better, so as to achieve the teaching objectives.
近年来人工智能(artificial intelligence，AI)技术在医学领域的应用发展迅猛，尤其在眼科领域，成果显著，极大地提高了相关影像数据的诊断效率，推动了该领域研究的进展。然而，大多数AI的应用都集中于成人眼病，在婴幼儿眼病方向的研究较少。究其原因，可能是婴幼儿眼部影像数据采集配合度低，部分影像设备应用受限，且相关领域专业眼科医生数量匮乏。然而，婴幼儿期是视觉发育最重要的阶段，也是出生缺陷早期筛防诊治的重灾区，对患儿的视觉发展具有长远且重要的影响，亟需AI相关产品提高婴幼儿眼病筛查效率，缓解医疗资源不足的现状。本文将对近年AI在婴幼儿眼病领域的研究应用现状、进展及存在的相关问题进行综述。
In recent years, the application of artificial intelligence (AI) in medicine, especially in ophthalmology, has developed rapidly with remarkable results. This has greatly improved the diagnostic efficiency of relevant imaging data and promoted further research in this field. However, most applications of AI are focused on adult eye diseases, and few studies have addressed infantile eye diseases. This may be because of the non-cooperative nature of infants, the limited availability of imaging equipment in infants, and the lack of pediatric ophthalmologists. Infancy is the most important stage of vision development. Disturbance during this period have a profound and lasting influence on vision development. Hence, early screening, diagnosis, and treatment of birth defects is important. AI-related products, which improves the efficacy of infant eye disease screening, are urgently needed. This paper reviews the current status, progress, and existing problems of recent research related to application of AI in infantile eye diseases.
