The accuracy of ultrasound examination and measurement is of great significance to diagnosing ocular disea-ses.The commonly used ocular ultrasonography includes amplitude(A)-mode ultrasound,brightness(B)-mode ultra-sound,ultrasound biomicroscopy(UBM),and color Doppler flow imaging(CDFI).A-mode ultrasound is mainly used to measure the distance between tissues with different echo intensities.B-mode ultrasound can visualize the two-dimensional structure of the eye and make quantitative measurements of the target point.UBM is an ultra-high-frequency two-dimen-sional imaging method,which can clearly display the structural characteristics of the anterior segment and measure the rele-vant parameters.It can also help quantitatively analyze the morphological changes of the angle and the anterior segment be-fore and after implantable collamer lens surgery.CDFI can quantitatively measure the parameters of blood vessels by apply-ing the Doppler effect on the basis of two-dimensional ultrasound,reflecting the changes of blood flow in the eye.To standardize the operations of different ultrasound examination methods,this guideline is formulated.This guideline mainly focuses on the standardized operation of ocular ultrasound instruments and their clinical application in ocular diseases,so as to provide guidance for the diagnosis and treatment of related ocular diseases.

The rapid development of artificial intelligence(AI)technology has driven the intelligentization of medicine.In recent years,due to the continuous improvement of machine learning and deep learning technologies,AI technology has made rapid progress in the diagnosis and treatment of ocular fundus diseases,including retinal vascular disease,macular disease,retinal detachment,and retinal pigment degeneration.Early diagnosis and treatment are of great significance for the prognosis of ocular fundus diseases.This article gives a guide for the application of AI in automatic segmentation of ret-inal images and disease diagnosis,providing a reference for further research and appIication of AI in this field.

Senile macular degeneration(SMD)is a complex,highly heritable,and multifactorial disease that leads to the aging-related change in the macular region,characterized by progressive retinal degeneration and progressive loss of vi-sion.About 200 million people worldwide suffer from SMD,and the incidence is increasing as the population ages.Artifi-cial intelligence(AI)technology has developed rapidly in recent years,and its application in the medical field has brought new possibilities for the development of the medical industry.AI-based qualitative and quantitative evaluation of retinal fluid can not only facilitate the diagnosis of neovascular SMD but also help adjust the treatment plan timely according to the effect,so as to provide more targeted treatment for patients.This guide summarizes the application of AI in the treatment of SMD,including the application progress,clinical application and future development of AI in retinal fluid monitoring,to pro-vide sufficient support for ophthalmologists to evaluate patient's conditions,design treatment plans and estimate prognosis.

With the upsurge of artificial intelligence(AI)technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Special Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical AI models, and common indices and formulae for clinical AI model evaluation in detail, and amply elaborates the evaluation method of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.