With the increasing coverage and availability of smart phones, the application of realizing intelligent health management has become an emerging research hotspot. The new generation of smart phones can perform health analysis by tracking the step numbers, monitoring heart rate and sleep quality, taking photos and other approaches, thereby becoming a new medical aid tool. With the continuous development of deep learning technology in the field of image processing, intelligent diagnosis based on medical imaging has blossomed in many disciplines, which is expected to completely change the traditional eye diseases diagnosis and treatment mode of hospitals. The conventional diagnosis of ophthalmic diseases often relies on various forms of images, such as slit lamp biological microscope, fundus imaging, optical coherence tomography, etc. As a result, ophthalmology has become one of the fastest growing areas of medical artificial intelligence (AI). The deployment of ophthalmological AI diagnosis and treatment system on smart phones is expected to improve the diagnostic efficiency and screening coverage to relieve the strain of medical resources, which has a great development prospect. This review focuses on the prevention and telemedicine progress of eye diseases based on deep learning and smart phones, taking diabetic retinopathy, glaucoma and cataract as examples to describe the specific research, application and prospect of deep learning and smart phones in the management of eye diseases.

Objective: To evaluate the difference, correlation and agreement of eye parameters measured by StarEyes 900 visual function analyzer (Wan Ling Bang Qiao, China) and IOLMaster 500 (Carl Zeiss, Germany) swept-source optical coherence tomography biometer. Methods: A prospective study was designed involving 62 healthy subjects (124 eyes) undergoing ophthalmic examinations in Zhongshan Ophthalmic Center from June 2021 to July 2021. Data from their both eyes were selected for analysis in all patients. Axial length (AL), keratometry for the steepest meridian (Ks), keratometry for the flattest meridian (Kf), mean keratometry (Km) and corneal diameter (WTW) were measured by the StarEyes 900 visual function analyzer and IOLMaster 500 swept-source optical coherence tomography biometer. A paired t-test was used to analyze the differences in measurement results. The Pearson correlation coefficient was used to analyze the correlation. Bland-Airman method was used to assess the agreement of the instruments. Results: The AL, Kf, Ks, Km and WTW obtained by StarEyes 900 and IOLMaster 500 were (24.18±1.08) mm and (24.16±1.08) mm, (42.84±1.65) D and (43.04±1.57) D, (44.34±1.90) D and (44.17±1.80) D, (43.59±1.73) D and (43.61±1.64) D, and (11.64±0.29) mm and (11.64±0.30) mm, respectively. The Km and WTW of the two devices showed no significant difference (P>0.05), while the AL, Ks and Kf showed significant differences (all P<0.01). The AL and Ks obtained by StarEyes 900 were higher than by IOLMaster 500, while the Kf, Km and WTW were lower. The measurements of five aforementioned biometric parameters by both devices showed good correlation by Pearson correlation coefficient and good agreement by Bland-Airman. Conclusion: The Km and WTW measured by the two devices showed no significant difference, and provided references to one another. The difference in AL, Kf and Ks between the two devices showed significant differences. All of the measurements showed good correlation by Pearson correlation coefficient and good agreement by Bland-Airman.