Artificial intelligence (AI) has been widely applied and promoted in ophthalmology, and has gradually become a research hotspot in the diagnosis and treatment of many common ophthalmopathies, including diabetic retinopathy, cataract, glaucoma, and retinopathy of prematurity. AI improves the shortage of medical care, the lack of diagnostic criteria and the low level of diagnosis and treatment technology, and explores a “new race track” for cataract diagnosis and treatment. The purpose of this article is to review the application status, progress and limitations of AI in the diagnosis and treatment of cataract, aiming to provide more information for further development, application and promotion of AI in the field of cataract.

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.

With the widespread application of minimally invasive vitrectomy and the improvement of surgical techniques, the demands of patients for better postoperative visual quality are increasing. Cataract is the most common complication after vitrectomy, whereas the refractive outcomes of cataract patients with prior vitrectomy are viable and difficult to predict. In this paper, the main factors affecting postoperative refractive error of cataract patients with a history of vitrectomy, such as biometric error, selection of intraocular lens calculation formulas and prediction of effective lens position, were reviewed in order to provide reference for reducing postoperative refractive error of this special group of cataract patients.

Objective: To compare the accuracy of 10 intraocular lens (IOL) power calculation formulas in patients undergoing combined silicone oil removal and cataract surgery, biometry is performed using the swept-source optical coherence tomography biometer OA-2000. Methods: A retrospective analysis. A total of 62 patients (62 eyes) who underwent combined silicone oil removal and cataract surgery in Zhongshan Ophthalmic Center, Sun Yat-sen University from March to July in 2021 were enrolled. Preoperative biometry was performed by OA-2000 in all patients. New-generation formulas (Barrett Universal II [BUII], Emmetropia Verifying Optical [EVO] 2.0, Hill-Radial Basis Function [Hill-RBF] 3.0, Hoffer QST, Kane and Pearl-DGS) and traditional formulas (Haigis, Hoffer Q, Holladay 1 and SRK/T) were evaluated. The median absolute prediction error (MedAE) and mean absolute prediction error (MAE) were the main parameters used to assess accuracy. Subgroup analyses were performed based on the axial length of 23 mm and 26 mm. Results: Six new-generation formulas, Haigis, and SRK/T showed myopic shift (-0.47 ~ -0.27 D, P<0.05), while no systematic bias was found in Hoffer Q and Holladay 1 displayed (P>0.05). The smallest MedAE (0.55 D) and MAE (0.81 D) were found in Kane formula, but there was no statistically significant difference compared with other formulas (P>0.05). The myopic shift (-1.46 ~ -1.25 D, P<0.05) in eyes shorter than 23 mm were found in all formulas, while there was no significant systematic bias (-0.32 ~ 0.41 D, P>0.05) in other subgroups. In axial length shorter than 23 mm, the Pearl-DGS formula stated the smallest MedAE (0.97 D) and MAE (1.26 D), and was significantly more accurate than Hill-RBF 3.0 (P=0.01) and SRK/T (P=0.02). In eyes with an axial length between 23 mm and 26 mm, the Kane formula had the lowest MedAE (0.44 D) and MAE (0.66 D). No significant difference was found in eyes longer than 26 mm. Conclusion: The Kane formula showed the highest accuracy in patients undergoing combined silicone oil removal and cataract surgery measured by OA-2000, whereas the Pearl-DGS formula could be more accurate in eyes with an axial length shorter than 23 mm.

Laser-assisted in situ keratomileusis (LASIK) is a crucial corneal refractive surgery for correcting refractive errors. The cornea, after undergoing excimer laser ablation, undergoes changes in biometric measurements. For such patients, conventional measurements and IOL power calculations based on standard formulas may no longer be accurate, leading  to significant postoperative refractive errors and subsequently impacting the patient's visual quality. This article presents a case of a 46-year-old male cataract patient who had a history of refractive errors in both eyes and had previously undergone LASIK surgery. Preoperative corneal topography revealed corneal eccentric ablation in both eyes, posing challenges in determining IOL power. The surgeon assessed the centration of corneal ablation using corneal topography, selected the keratometry value (K value) within specific corneal regions, and calculated the IOL power using the Barrett True K formula. Postoperatively, the cataract patient experienced relatively minor refractive errors, leading to improved vision and enhanced visual quality.

In recent years, electrical stimulation of the eye (ES) has gradually revealed its potential therapeutic value in a variety of retinal diseasesin different directions. Among them, transcorneal electrical stimulation (TES), as a non-invasive treatment, can have a positive effect on the retina, optic nerve, fundus vessels and related structures. TES can improve vision, show positive effects in protecting photoreceptor cells and slowing disease progression, improve the quality of life of patients, and can regulate neuronal activity in the brain without damaging the eyeball, providing a new option for the treatment of retinal diseases. The research on the application on TES on retinitis pigementosa (RP), age-related macular degeneration (AMD), retinal angiopathy, glaucoma and optic neuropathy are reviewed in this article. It is found in the study that TES therapy is a safe and surgery-free adjuvant therapy tool, and has a wide application prospect. The purpose of this article is to provide clinicians with a comprehensive overview of TES research,and to explore its potential application value in the field of ophthalmology. However, the specific mechanism of TES therapy still needs to be further explored in order to better apply in clinical practice. At the same time, future studies should also focus on the effect of combining TES with other treatment methods, in order to provide more effective treatment options for patients.

Objective: To establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. Methods: The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three step strategy: (1)capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services. Results: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3)detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3%  of people be ’referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern. Conclusions: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

patient, male, 1year and 9months old, was first diagnosed as “eye tumor” in the ophthalmology department and requested for excision. But it was diagnosed as multiple Langerhans cell histiocytosis (LCH) through imaging and pathological examination ultimately.Bone lytic changes appeared in many parts of the whole body, which did not meet the indication of excision.The tumor was smaller after systemic chemotherapy. The diagnosis and treatment of this case suggests ophthalmologists that eye tumors can be caused by caused by systemic diseases, systemic diseases. During operation, it is recommended to perform pathological biopsy to avoid treatment delay.

Thyroid-associated ophthalmopathy (TAO), known as Graves’orbitopathy, is an organ specific autoimmune disease closely related to thyroid diseases. Exophthalmos is one of the main clinical manifestations of thyroid related ophthalmopathy and is also the reason for most patients seeking medical atention in clinical practice.Eyeball protrusion can afect aesthetics on the one hand, and on the other hand, it can lead to exposed keratitis due to incomplete closure of the eyelids or compressive optic neuropathy due to increased orbital pressure.Orbital decompression has been used to treat severe TAO that threatens vision for over 100 years, and its safety and efectiveness have been confrmed.However, postoperative new diplopia remains a challenge for many ophthalmic medical workers.In recent years, many studies have explored the relevant factors of postoperative new diplopia, and improved the surgery, achieving varying degrees of progress in reducing postoperative new diplopia.Tis article reviews the research progress of new diplopia afer orbital decompression, aiming to promote more accurate surgery for thyroid related eye diseases by specialized doctors.

Congenital cataract (CC) is one of the most common causes of pediatric visual impairment.As our understanding of CC's etiology, clinical manifestations, and pathogenic genes deepens,various CC categorization systems based on diferent classifcation criteria have been proposed.Regrettably, the application of the CC category in clinical practice and scientifc research is limited. It is challenging to obtain preciseinformation that could guide the timely treatment decision-making for pediatric cataract patients or predict their prognosis from a specificCC classification. This review aims to discuss the statusquo of CC categorization systems and the potential directions for future research in this field, focusingon categorization principles and scientificapplication in clinical practice.Additionally, it aims to propose the potential directions for future research in this domain.