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Distal Arthrogryposis type 5D (DA5D) is a rare genetic disease, expressed phenotypically by skeletal and ocular abnormalities.Two sisters, ages 42 and 46 years old, were ascertained, both diagnosed with arthrogryposis and unusual ophthalmic late expressions of the disease. They were examined and followed up by both ophthalmologists and medical geneticists. Molecular analysis was performed and population screening followed among healthy individuals of the same ethnic background who reside in the same village.The two sisters expressed myogenic ptosis with poor levator palpebrae function, limitation in up gaze, lagophthalmos, refractive errors, corneal scarring and vascularization along with severe distal arthrogryposis. The newly reported features were: significant lower lid retraction, causing inferior scleral show. Sanger sequencing of the coding regions of ECEL1 gene revealed a homozygous deletion of 46 bps. The carrier frequency is 1:24 (4.2% carriers) in the probands' village.We diagnosed two patients with DA5D carrying a homozygous pathogenic genetic variant previously reported only once. We report the late ophthalmologic manifestations of this rare disorder and emphasize the importance to recognize possible long-term ophthalmic complications. Measures are needed to diagnose this rare disorder at a younger age and to address ophthalmic and orthopedic complications that might be prevented. We revealed the causative genetic variant and a carrier frequency of 1:24 for DA5D, in the probands' village, thus enabling accurate genetic counselling and justifying genetic testing to the residents of this village as a diagnostic and preventive measure.

Objective: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale. Methods: A total of 1,245 images were graded for quality by two ophthalmologists within the range 1-10, with a resolution of 0.5. A DL regression model was trained for fundus image quality assessment. The architecture used was Inception-V3. The model was developed using a total of 89,947 images from 6 databases, of which 1,245 were labeled by the specialists and the remaining 88,702 images were used for pre-training and semi-supervised learning. The final DL model was evaluated on an internal test set (n=209) as well as an external test set (n=194). Results: The final DL model, denoted FundusQ-Net, achieved a mean absolute error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary classification model on the public DRIMDB database as an external test set the model obtained an accuracy of 99%. Significance: the proposed algorithm provides a new robust tool for automated quality grading of fundus images., Comment: 12 pages, 9 figures, published in Computer Methods and Programs in Biomedicine