Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study

2021 ◽  
Author(s):  
Chenxi Zhang ◽  
Feng He ◽  
Bing Li ◽  
Hao Wang ◽  
Xixi He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pilot Study ◽  
Retinal Detachment ◽  
Learning System ◽  
Wide Field ◽  
Fundus Images ◽  
Retinal Breaks ◽  
Lattice Degeneration

scholarly journals A deep learning system for identifying lattice degeneration and retinal breaks using ultra-widefield fundus images

2019 ◽  
Vol 7 (22) ◽  
pp. 618-618 ◽  
Author(s):  
Zhongwen Li ◽  
Chong Guo ◽  
Danyao Nie ◽  
Duoru Lin ◽  
Yi Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning System ◽  
Fundus Images ◽  
Retinal Breaks ◽  
Lattice Degeneration

scholarly journals Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Chong Guo ◽  
Danyao Nie ◽  
Duoru Lin ◽  
Yi Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Retinal Detachment ◽  
Large Scale ◽  
Global Scale ◽  
Clinical Applications ◽  
Learning System ◽  
Vision Impairment ◽  
Fundus Images ◽  
Severe Visual Loss ◽  
Visual Results

AbstractRetinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽  
2020 ◽  
Author(s):  
Alanna Ebigbo ◽  
Robert Mendel ◽  
Tobias Rückert ◽  
Laurin Schuster ◽  
Andreas Probst ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pilot Study ◽  
White Light ◽  
Submucosal Invasion ◽  
Learning System ◽  
Endoscopic Images ◽  
Barrett’S Cancer ◽  
Significant Difference ◽  
Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

Rhegmatogenous retinal detachment secondary to isolated retinal metastasis from small cell lung carcinoma: An uncommon complication

2020 ◽  
pp. 112067212090202
Author(s):  
Ahmet Yucel Ucgul ◽  
Sengul Ozdek ◽  
Murat Hasanreisoğlu ◽  
Kubra Aydın ◽  
Hatice Tuba Atalay
Keyword(s):  
Retinal Detachment ◽  
Lung Carcinoma ◽  
Multimodal Imaging ◽  
Rhegmatogenous Retinal Detachment ◽  
Small Cell Lung Carcinoma ◽  
Wide Field ◽  
Cell Lung Carcinoma ◽  
Retinal Breaks ◽  
Prophylactic Radiotherapy ◽  
Uncommon Complication

Purpose: To report a case of rhegmatogenous retinal detachment associated with isolated retinal metastasis from lung carcinoma. Methods: Multimodal imaging, including wide-field retinal imaging, ultrasonic imaging, and magnetic resonance imaging. Results: Systemic chemotherapy and cranial prophylactic radiotherapy resulted in shrinkage of these lesions and retinal breaks making them much smaller and preventing progression of retinal detachment transiently. Conclusion: This is the first reported case of rhegmatogenous retinal detachment secondary to retinal metastasis from a lung cancer.

scholarly journals A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yongli Xu ◽  
Man Hu ◽  
Hanruo Liu ◽  
Hao Yang ◽  
Huaizhou Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
High Sensitivity ◽  
Learning System ◽  
Small Samples ◽  
Fundus Images ◽  
Fiber Layer ◽  
Glaucoma Diagnosis ◽  
Diagnostic Thinking

AbstractThe application of deep learning algorithms for medical diagnosis in the real world faces challenges with transparency and interpretability. The labeling of large-scale samples leads to costly investment in developing deep learning algorithms. The application of human prior knowledge is an effective way to solve these problems. Previously, we developed a deep learning system for glaucoma diagnosis based on a large number of samples that had high sensitivity and specificity. However, it is a black box and the specific analytic methods cannot be elucidated. Here, we establish a hierarchical deep learning system based on a small number of samples that comprehensively simulates the diagnostic thinking of human experts. This system can extract the anatomical characteristics of the fundus images, including the optic disc, optic cup, and appearance of the retinal nerve fiber layer to realize automatic diagnosis of glaucoma. In addition, this system is transparent and interpretable, and the intermediate process of prediction can be visualized. Applying this system to three validation datasets of fundus images, we demonstrate performance comparable to that of human experts in diagnosing glaucoma. Moreover, it markedly improves the diagnostic accuracy of ophthalmologists. This system may expedite the screening and diagnosis of glaucoma, resulting in improved clinical outcomes.

Sign in / Sign up

Export Citation Format

Share Document