Distribution and causes of blindness and severe visual impairment in children at a tertiary referral centre in Rwanda

The Ayounkon project: visual impairment, eye diseases and unmet eye care needs in the Syrian refugee population and the hosting community in Lebanon

AIM

To report on the distribution of eye diseases, aetiologies of severe visual impairment/blindness (SVI/BL) and unmet eye care needs of the Syrian refugee population and the Lebanese host community.

METHODS

This retrospective study analysed the data of patients that were examined during the Ayounkon project -an eye health care project offering medical and surgical treatment for Syrian refugees and Lebanese host community in the Bekaa Valley in Lebanon. The project took place in three different primary health care centres and involved cooperation between several NGOs and ophthalmologists working on a voluntary basis. Data was analysed for distribution of eye diseases and aetiologies responsible for monocular and binocular SVI/BL.

RESULTS

A total of 2067 patients were included, 677 were children < 18 years. The most frequent pathologies were ocular allergy (10%), and cataract (7.4%). 158 patients (7.6%) were referred for surgery. Glasses were prescribed for 1103 patients (53.4%), of whom 242 (21.9%) were children of school age. SVI/BL was found in 276 patients (13%). The condition was bilateral in 116 patients (42%). SVI/BL was significantly more frequent in the Syrian population than in the Lebanese (186 patients, 14.8% versus 86 patients, 11.3%; p = 0.04). The main causes for SVI/BL were cataract, keratoconus/corneal decompensation and amblyopia.

CONCLUSION

The Syrian refugee population and the Lebanese host community have a high prevalence of ophthalmic pathologies and SVI/BL. Visual impairment is more prevalent in the refugee population. Our findings underscore the importance of targeted interventions and access to eye care services for these populations.

EyeCNN: exploring the potential of convolutional neural networks for identification of multiple eye diseases through retinal imagery

BACKGROUND

The eyes are the most important part of the human body as these are directly connected to the brain and help us perceive the imagery in daily life whereas, eye diseases are mostly ignored and underestimated until it is too late. Diagnosing eye disorders through manual diagnosis by the physician can be very costly and time taking.

OBJECTIVE

Thus, to tackle this, a novel method namely EyeCNN is proposed for identifying eye diseases through retinal images using EfficientNet B3.

METHODS

A dataset of retinal imagery of three diseases, i.e. Diabetic Retinopathy, Glaucoma, and Cataract is used to train 12 convolutional networks while EfficientNet B3 was the topperforming model out of all 12 models with a testing accuracy of 94.30%.

RESULTS

After preprocessing of the dataset and training of models, various experimentations were performed to see where our model stands. The evaluation was performed using some well-defined measures and the final model was deployed on the Streamlit server as a prototype for public usage. The proposed model has the potential to help diagnose eye diseases early, which can facilitate timely treatment.

CONCLUSION

The use of EyeCNN for classifying eye diseases has the potential to aid ophthalmologists in diagnosing conditions accurately and efficiently. This research may also lead to a deeper understanding of these diseases and it may lead to new treatments. The webserver of EyeCNN can be accessed at ( https://abdulrafay97-eyecnn-app-rd9wgz.streamlit.app/ ).