AIM:To investigate the challenges and needs of individual ophthalmologists participated in the training and their countries in the prevention and treatment of blindness and visual impairment, sum up the training effects, and discuss how to promote the development of eye health in the Belt and Road countries in the future.METHODS:A total of 48 ophthalmologists from 6 countries, including Kenya, Zambia, Nigeria, South Africa, Malawi, Botswana between August 1 and August 30, 2022, were trained and anonymous questionnaire survey was conducted. The questionnaire consists of 15 questions covering three aspects: challenges and strategies related to vision impairment and blindness in the country, training satisfaction, and recommendations for establishing optometric centers in Africa.RESULTS: A total of 48 questionnaires were distributed and 47 valid questionnaires were collected. The ophthalmologists hold the view that the biggest challenge of their countries in prevention and treatment of ocular diseases was high nursing costs, accounting for 36.17%, the biggest challenge faced by ophthalmologists was low wages, accounting for 29.79%. Building more eye specialist hospitals(38.30%)and providing more training opportunities(65.96%)can effectively help the countries and the ophthalmologists. The organizational satisfaction with the training courses reached 98%, the content and the lecturers' satisfaction were 100%.CONCLUSION:There are urgent needs to build more ophthalmic hospitals and provide more professional training opportunities to solve the difficulties in the prevention and treatment of eye diseases of the countries and the ophthalmologists. This training program has high satisfaction and good feedback.

Objective : To construct a Miller class Ⅲ gingival recession animal model and to lay the foundation for exploring the treatment of Miller class Ⅲ gingival recession. Methods: Two adult male beagle dogs were selected, and four teeth from each beagle dog were selected to establish an experimental Miller class Ⅲ gingival recession model. The root surface was revealed by removing the soft and hard tissues of the buccal side. The success of the model was determined by measuring the vertical gingival retraction (VGR), horizontal retraction (HGR), keratosis tissue width (KTW), gingival tissue thickness (GTT), and probing depth (PD) at 1, 2, 4, 6, and 8 weeks after modeling. Results: After observing the clinical indexes, the PDs before and after the modeling were all smaller than 3 mm and no deep-period pockets were formed. The VGR before modeling was 0 mm, and the VGR range after modeling was 5-6.38 mm. A comparison of the before and after modeling results showed that this difference was statistically significant (P < 0.05). The postoperative VGR results were grouped according to timepoint. A comparison between the two groups showed that the differences at 2, 4, 6 and 8 weeks postoperatively were not statistically significant (P > 0.05). The HGR before the modeling was 0 mm, and the HGR fluctuated around 10.5 mm after the modeling, and this difference was statistically significant (P < 0.05). The HGR results were grouped by timepoint after surgery, and a one-way analysis of showed that the differences between the two groups were not statistically significant (P > 0.05). The KTW range before modeling was 6~9 mm, and it fluctuated around 2 mm after modeling, and this difference was statistically significant (P < 0.05). The KTW results were grouped by timepoint after surgery, and they indicated that significant differences did not occur between the groups postoperatively (P > 0.05). The pre-modeling GTT was 1.5 mm, and the GTT range after modeling was 1.5-2 mm. The preoperative and postoperative GTT results were grouped by timepoint, and the results showed that significant differences did not occur between 1 week and 2 weeks after surgery (P = 0.123), although a statistically significant difference was observed at 1 week postoperatively between this group and the other groups (P < 0.05). Conclusion The method used in this experiment can successfully build a Miller class III gingival recession animal model, and the model remains stable after wound healing.

AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included, and 13 470 infrared pupil images were collected for the study. All infrared images for pupil segmentation were labeled using the Labelme software. The computation of pupil diameter is divided into four steps: image pre-processing, pupil identification and localization, pupil segmentation, and diameter calculation. Two major models are used in the computation process: the modified YoloV3 and Deeplabv3+ models, which must be trained beforehand.RESULTS:The test dataset included 1 348 infrared pupil images. On the test dataset, the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils. The DeeplabV3+ model achieved a background intersection over union(IOU)of 99.23%, a pupil IOU of 93.81%, and a mean IOU of 96.52%. The pupil diameters in the test dataset ranged from 20 to 56 pixels, with a mean of 36.06±6.85 pixels. The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels, with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm, proven to be highly accurate and reliable for clinical application.