近年来随着医疗领域数字化、信息化建设的加速推进，人工智能的应用越来越广泛，在眼科医学方面尤为突出。婴幼儿处于视觉系统发育的关键时期，此时发生的眼病往往会造成不可逆的视功能损伤，带来沉重的家庭和社会负担。然而，由于婴幼儿群体的特殊性以及小儿眼科医生的短缺，开展大规模小儿眼病筛查工作十分困难。最新研究表明：人工智能在先天性白内障、先天性青光眼、斜视、早产儿视网膜病变以及视功能评估等领域已经得到相关应用，在多种婴幼儿眼病的早期筛查、诊断分期、治疗建议等方面都有令人瞩目的表现，有效解决了许多临床难点与痛点。但目前婴幼儿眼科人工智能仍然不如成年人眼科发展充分，亟须进一步的探索和研发。
In recent years, with the acceleration of digitalization and informatization in medical field, artificial intelligence (AI) is more and more widely applied, especially in ophthalmology. Infants are in the critical period of visual development, during which eye diseases can lead to irreversible visual impairment and bring heavy burden to family and society. Due to the particularity of infants and the shortage of pediatric ophthalmologists, it is challenging to carry out large-scale screening for eye diseases of infants. According to the latest studies, AI has been studied and applied in the fields of congenital cataract, congenital glaucoma, strabismus, amblyopia, retinopathy of prematurity, and evaluation of visual function, and it has achieved remarkable performance in the early screening, diagnosis stage and treatment suggestions, solving many clinical difficulties and pain points effectively. However, AI for infantile ophthalmology is not as developed as for adult ophthalmology, so it needs further exploration and development.
目的：探讨应用数字化广域成像系统RetCam3行婴幼儿口服荧光素钠眼底血管荧光造影(fluorescein fundus angiography，FFA)的护理。方法：选择2018年8月至2019年12月在广州中山大学中山眼科中心小儿眼病综合科就诊的眼底疾病婴幼儿78例，应用RetCam3进行口服法FFA检查及护理，将护理要点进行总结。结果：所有患儿安全、顺利完成检查，检查过程中均未发生异常病情变化或与检查、药物相关的并发症。经FFA确诊家族性渗出性玻璃体视网膜病变(familial exudative vitreoretinopathy ，FEVR)26例，早产儿视网膜病变(retinopathy of prematurity ，ROP)23例，色素失禁症患者6例；玻璃体积血患者3例；视网膜母细胞瘤患者3例；牵牛花综合征患者1例；视网膜色素变性患者3例；弓蛔虫眼病患者1例；原始永存玻璃体患者2例；不明原因眼底病变患者5例，单眼视网膜皱襞患者1例，先天性小眼球患者1例，巨细胞病毒感染患者1例，先天性黄斑发育不良患者1例；Coats病患者1例。结论：应用RetCam3行婴幼儿口服法FFA是一种安全、有效的检查方法。规范、恰当的护理配合能够保证检查准确、顺利地完成。
Objective: To share the nursing experience of RetCam3 ultra-widefield oral fluorescein fundus angiography (FFA) in infants with fundus diseases. Methods: Seventy-eight infants with fundus diseases admitted to General Department of Pediatric Ophthalmology in Zhongshan Ophthalmic Center, Sun Yat-sen University from August 2018 to December 2019 were recruited. Oral FFA was carried out using the 130-degree lens of RetCam3, and the key points of nursing were summarized. Results: No complications related to the examination and drugs occurred after oral FFA with an appropriate nursing manner. FFA confirmed 26 cases of familial exudative vitreoretinopathy,23 cases of retinopathy of prematurity and 6 cases of pigment incontinence. Vitreous hematoma was observed in 3 patients, retinoblastoma in 3 patients, Morning Glory syndrome in 1 patient, retinitis pigmentosa in 3 patients,Ascaris lumbricoides eye disease in 1 case, original permanent vitreous body in 2 patients, unexplained fundus lesions in 5 patients, monocular retinal fold in 1 patient, congenital micro-eyeball in 1 patient, cytomegalovirus infection in 1 patient, congenital macular dysplasia 1 patient and Coats disease in 1 patient. Conclusion: Oral FFA with RetCam3 is an effective and safe detection method for infants. Standard and proper nursing can ensure the examination can be performed accurately and smoothly.
克服现有婴幼儿眼科手术病号服存在的穿脱不便、容易着凉、无法避免患儿抓挠术眼等问题，提供一种便于穿脱、保护胸腹部和术眼的婴幼儿眼科手术病号服*。
Abstract Present patient clothing for infants and children with ophthalmic surgery have several limitations, which is inconvenient to wear, hard to keep warm and difficult in preventing patients from scratching eyes underwent surgery. A modified patient clothing for infants and children is designed to overcome these existing problems.
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.
