A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16) 2 , BCNN (VGG19) 2 , and BCNN (Inception_V3) 2 , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.

Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning.

Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common eye diseases. They are often undiagnosed or diagnosed late. This can result in permanent and irreversible vision loss. Therefore, early detection and treatment of these diseases can prevent vision loss, save money, and provide a better quality of life for individuals. Optical coherence tomography (OCT) imaging is widely applied to identify eye diseases, including DME and AMD. In this work, we developed automatic deep learning-based methods to detect these pathologies using SD-OCT scans. The convolutional neural network (CNN) from scratch we developed gave the best classification score with an accuracy higher than 99% on Duke dataset of OCT images.

Outcomes of transconjunctival 23-gauge vitrectomy for diabetic tractionnal retinal detachment.

INTRODUCTION: The transconjunctival 23-gauge vitrectomy without sutures (VTCSS) combines the advantages of the 20 and 25-gauge system. It currently represents the gold standard for the surgical management of vitreoretinal pathologies, especially in diabetic patients. AIM: Evaluate the anatomical and functional results of the VTCSS 23 gauge in diabetic tractionnal retinal detachment (DRT). METHODS: This is a retrospective study of patients undergoing vitrectomy 23 gauges from February 2015 to February 2017. Each patient a complete ophthalmological examination with collection of  pre operative peroperative and postoperative data. The vitrectomy was performed by 3 ways through the 23-gauge cannula system Results :We collected 52 eyes presenting a DRT. The average age of patients was 50.62 years old. The average glycated hemoglobin (HBA1C) was 9.3%. The average time to treatment was 76.92 days. The results were as follows: For anatomical results: anatomical success rate was 92.30%. Functionally: A significant improvement in mean AV from 1/80 to 1.25 / 10 after surgery (p = 0.022), a statistically significant correlation between the final postoperative VA and the management delay (p <0.001); significant correlation between preoperative VA and final postoperative VA (p <0.001). CONCLUSION: The VTCSS 23 gauge is an effective and safe surgery for DRT  surgery in diabetic patients with a gain in time and comfort for the patient.

[Management of ovarian cyst associated to pregnancy]. / Prise en charge du kyste de l'ovaire et grossesse: a propos de 25 cas.

BACKGROUND: The problem of the association of the ovarian cyst and pregnancy is to determine whether the cyst is functional or organic? AIM: To draw up the epidemiological profile of the patients having a cyst of the ovary, show the peculiarities of this association; and to clarify the therapeutic methods and its effects on the progress of the pregnancy. METHODS: A retrospective study concerning a period of 5 years and interesting 25 patients. RESULTS: The average age of the patients was of 34 years, the average parity was of 2. The discovery of the cyst was in 68% of cases in the first 3 months. On the clinical plan the circumstances of discovery were pelvic pains in 48% of cases and complications in 6% of cases; such us twisting of the cyst. 61% of the patients had a laparoscopy, 44% a laparotomy and a case of guided ultrasound punction, 4% of the patients underwent a pregnancy interruption, 17 pregnancies were led till the end, we noted an intrauterine death of the foetus and a case of late abortion at 22SA. CONCLUSION: problems due to the association of the ovarian cyst and pregnancy are especially of diagnostic and therapeutic order. Obsession was to underestimate a malignant tumour; that's why we should perform a surgical investigation in front of any persevering cyst beyond the first three months of the pregnancy.