Pharmacovigilance investigators, a social network, and basic scientists can collaborate on pharmacovigilance investigations. Revised product labels describing a new serious adverse drug reaction, levofoxacin-associated long-term disability, as recommended by an FDA advisory committee, are advised.
Purpose:
For diagnosing glaucomatous damage, we have employed a novel convolutional neural network (CNN) from TrueColor confocal fundus images to conquer the black box dilemma in artificial intelligence (AI). This neural network with CNN architecture with human-in-the-loop (HITL) data annotation helps not only in diagnosing glaucoma but also in predicting and locating detailed signs in the glaucomatous fundus, such as splinter hemorrhages, glaucomatous optic atrophy, vertical glaucomatous cupping, peripapillary atrophy, and retinal nerve fiber layer (RNFL) defect.
Methods:
The training was done on a well-curated private dataset of 1,400 high-resolution confocal fundus images, out of which 1,120 images (80%) were used exclusively for training and 280 images (20%) were used exclusively for testing. A custom trained You Only Look Once version 5 (YOLOv5)-based object detection methodology was used to identify the underlying conditions precisely. Twenty-six predefined medical conditions were annotated by a team of humans (comprising two glaucoma specialists and two optometrists) by using the Microsoft Visual Object Tagging Tool (VoTT) tool. The 280 testing images were split into three groups (90,100, and 90 images) for three test runs done once every 15 days.
Results:
Test results showed consistent increments in the accuracy, from 94.44% to 98.89%, in predicting the glaucoma diagnosis along with the detailed signs of the glaucomatous fundus
Conclusion:
Utilizing human intelligence in AI for detecting glaucomatous fundus images by using HITL machine learning has never been reported in the literature before. This AI model not only has good sensitivity and specificity in accurate glaucoma predictions but is also an explainable AI, thus overcoming the black box dilemma.
Extended reality is one of the leading cutting-edge technologies, which has not yet fully set foot into the field of ophthalmology. The use of extended reality technology especially in ophthalmic education and counseling will revolutionize the face of teaching and counseling on a whole new level. We have used this novel technology and have created a holographic museum of various anatomical structures such as the eyeball, cerebral venous system, cerebral arterial system, cranial nerves, and various parts of the brain in fine detail. These four-dimensional (4D) ophthalmic holograms created by us (patent pending) are cost-effectively constructed with TrueColor confocal images to serve as a new-age immersive 4D pedagogical and counseling tool for gameful learning and counseling, respectively. According to our knowledge, this concept has not been reported in the literature before.
Augmented reality (AR) has come a long way from a science-fiction concept to a science-based reality. AR is a view of the real, physical world in which the elements are enhanced by computer-generated inputs. AR is available on mobile handsets, which constitutes an essential e-learning platform. Today, AR is a real technology and not a science-fiction concept. The use of an e-ophthalmology platform with AR will pave the pathway for new-age gameful pedagogy. In this manuscript, we present a newly innovated AR program named “Eye MG AR” to simplify ophthalmic concept learning and to serve as a new-age immersive 3D pedagogical tool for gameful learning.
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