Prediction of postoperative visual acuity after vitrectomy for macular hole using deep learning–based artificial intelligence

2021 ◽  
Author(s):  
Shumpei Obata ◽  
Yusuke Ichiyama ◽  
Masashi Kakinoki ◽  
Osamu Sawada ◽  
Yoshitsugu Saishin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Visual Acuity ◽  
Deep Learning ◽  
Macular Hole ◽  
Postoperative Visual Acuity

scholarly journals Does Internal Limiting Membrane Peeling Size Matter?

2017 ◽  
Vol 1 (1) ◽  
pp. 27-33 ◽  
Author(s):  
David H. W. Steel ◽  
Yunzi Chen ◽  
James Latimer ◽  
Kathryn White ◽  
Peter J. Avery
Keyword(s):  
Visual Acuity ◽  
Optic Nerve ◽  
Macular Hole ◽  
Internal Limiting Membrane ◽  
Consecutive Series ◽  
Fiber Layer ◽  
Postoperative Visual Acuity ◽  
Wide Range ◽  
The Mean ◽  
Size Matter

Purpose: A variety of retinal topographical changes occur after internal limiting membrane (ILM) peeling for macular holes including a movement of the fovea toward the optic nerve. This study was carried out to assess the effect of the extent of ILM-peeled area on these changes and postoperative visual acuity. Methods: Prospective single-center study of a consecutive series of patients undergoing macular hole surgery. Preoperative and postoperative optical coherence tomography images were used to assess a variety of measures of retinal morphology. Transmission electron microscopy of the peeled ILM was used to assess residual retinal and vitreous side debris. The area of the ILM peeled was calculated from intraoperative images. Results: Fifty-six eyes of 56 patients were included. The mean area of ILM peeled was 9.5 mm2 (2.4-28.3 mm2). The mean disc-to-fovea distance (DFD) preoperatively was 3703 μm with a mean reduction of 52 μm postoperatively, representing a change of −1.29% with a wide range of −7.04% to 1.36%. Using stepwise linear regression, ILM-peeled area was significantly associated with a change in DFD ( P < .001), extent of a dissociated optic nerve fiber layer appearance ( P < .001), and postoperative visual acuity ( P = .025). Nasotemporal retinal thickness asymmetry was associated with the minimum linear diameter ( P < .001). Conclusion: The ILM-peeled area has a significant effect on changes in retinal topography and postoperative visual acuity separate from macular hole size. Further study is needed to assess the effect of ILM peeled size on visual function and to guide clinical practice.

scholarly journals Deep Learning Model to Predict Postoperative Visual Acuity from Preoperative Multimedia Ophthalmic Data

2020 ◽  
Vol 9 (0) ◽  
pp. 241-248
Author(s):  
Ryo Otsuki ◽  
Osamu Sugiyama ◽  
Yuki Mori ◽  
Masahiro Miyake ◽  
Shusuke Hiragi ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Deep Learning ◽  
Learning Model ◽  
Postoperative Visual Acuity ◽  
Deep Learning Model

scholarly journals Artificial Intelligence and OCT Angiography in Full Thickness Macular Hole. New Developments for Personalized Medicine

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2319
Author(s):  
Stanislao Rizzo ◽  
Alfonso Savastano ◽  
Jacopo Lenkowicz ◽  
Maria Cristina Savastano ◽  
Luca Boldrini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Visual Acuity ◽  
Macular Hole ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Full Thickness ◽  
Cross Sectional ◽  
Vascular Plexus ◽  
Single Center Study ◽  
Full Thickness Macular Hole

Purpose: To evaluate the 1-year visual acuity predictive performance of an artificial intelligence (AI) based model applied to optical coherence tomography angiography (OCT-A) vascular layers scans from eyes with a full-thickness macular hole (FTMH). Methods: In this observational cross-sectional, single-center study, 35 eyes of 35 patients with FTMH were analyzed by OCT-A before and 1-year after surgery. Superficial vascular plexus (SVP) and deep vascular plexus (DVP) images were collected for the analysis. AI approach based on convolutional neural networks (CNN) was used to generate a continuous predictive variable based on both SVP and DPV. Different pre-trained CNN networks were used for feature extraction and compared for predictive accuracy. Results: Among the different tested models, the inception V3 network, applied on the combination of deep and superficial OCT-A images, showed the most significant differences between the two obtained image clusters defined in C1 and C2 (best-corrected visual acuity [BCVA] C1 = 49.10 [±18.60 SD] and BCVA C2 = 66.67 [±16.00 SD, p = 0.005]). Conclusions: The AI-based analysis of preoperative OCT-A images of eyes affected by FTMH may be a useful support system in setting up visual acuity recovery prediction. The combination of preoperative SVP and DVP images showed a significant morphological predictive performance for visual acuity recovery.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals An overview of artificial intelligence/deep learning

Pathology ◽  
2021 ◽  
Vol 53 ◽  
pp. S6
Author(s):  
Jack Garland ◽  
Mindy Hu ◽  
Kilak Kesha ◽  
Charley Glenn ◽  
Michael Duffy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning
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