Training ambidexterity – A survey-based analysis on the dexterity of ophthalmologists in performing standard ophthalmic procedures

Purpose: Our study was designed to determine ophthalmologists’ dexterity in performing standard ophthalmic procedures at various levels of expertise via a structured questionnaire. Methods: A structured questionnaire was administered via the Google platform from August 20 to September 19, 2022, to assess the perspectives and preferences of ophthalmologists concerning their proficiency in using their right hand, left hand, or both hands to perform routine tasks required in the practice of ophthalmic medicine and surgery. Results: Two hundred and three participants took part in the survey. A majority (n = 162, 79.8%) of the clinicians considered themselves right?handed, nine (4.4%) considered themselves left?handed, and 32 (15.7%) considered themselves ambidextrous. Also, 86% (n = 174) of the participants considered ambidexterity an essential trait in the practice of ophthalmic medicine and surgery. The number of cataract surgeries performed had an impact on the comfort of using both hands for performing anterior vitrectomy (P < 0.001), injection of viscoelastic (P < 0.001), and toric marking (P < 0.05), but not on the performance of capsulorhexis and switching of foot pedals. The number of procedures carried out had an impact on the comfort of using both hands in gonioscopy (P < 0.001), 90 D evaluation (P < 0.001), and 20 D evaluation (P < 0.05). More years of experience had an impact on skills involving the use of both hands for slit lamp joystick usage (P < 0.05) and laser procedures (P < 0.001). Conclusion: Irrespective of a person’s handedness, trained ambidexterity in the required fields is achievable and has a significant impact on one’s ability to perform the required skill optimally and appropriately.

Decoding glaucoma module premium edition

Background: Traditional methods for neuroretinal rim width measurement in spectral domain optical coherence tomography (SD-OCT) employs the Bruch‘s membrane opening (BMO) as the anatomical border of the rim, referenced to a BMO horizontal reference plane, termed as “Bruch’s Membrane Opening-Horizontal Rim Width” (BMO-HRW). BMO-HRW is defined as the distance between BMO and internal limiting membrane (ILM) on the horizontal plane. In contrast, the Spectralis OCT (Heidelberg Engineering, Germany) employs a new parameter called “Bruch’s Membrane Opening–Minimum Rim Width” (BMO-MRW) with Glaucoma Module Premium Edition (GMPE). GMPE provides a novel objective method of optic nerve head (ONH) analysis using BMO, but the neuroretinal rim assessment is performed from the BMO to the nearest point on the ILM, rather than on the horizontal reference plane. It is the BMO-MRW and is defined as the minimum distance between the BMO and ILM in the ONH. Purpose: In this video, anatomy of the ONH and GMPE is decoded from a neophyte user’s point of view, as to why BMO-MRW is more important than the traditional BMO-HRW for glaucoma evaluation. Synopsis: The GMPE concepts are depicted in a novel dynamic (Clinical vs OCT Vs Histology) screenplay, detailing the below focal points with 2D & 3D animations: True Margin of ONH, Bruch’s Membrane (BM), Histology Vs OCT, BMO, Bruch’s Membrane Opening-Minimum Rim Width, Bruch’s Membrane Opening-Minimum Rim Width Versus Bruch’s Membrane Opening-Horizontal Rim Width, Alpha, Beta, Gamma Zone of ONH in OCT, Anatomic Positioning System, Impact of Fovea Bruch’s Membrane Opening Centre Axis. Highlights: This video also highlights, how with the advent of Anatomic Positioning System, scans were able to align relative to the individual’s Fovea-to-BMO-center (FoBMOC) axis at every follow-up, for accurately detecting changes, as small as 1 micron in BMO-MRW, thus creating a new world in diagnosing glaucoma and detecting glaucomatous progression with precision.

Under lock and key: Incorporation of blockchain technology in the field of ophthalmic artificial intelligence for big data management - A perfect match?

Big data has been a game changer of machine learning. But, big data is a form of centralized version of data only available and accessible to the technology giants. A way to decentralize this data and make machine learning accessible to the smaller organizations is via the blockchain technology. This peer?to?peer network creates a common database accessible to those in the network. Furthermore, blockchain helps in securing the digital data and prevents data tampering due to human interactions. This technology keeps a constant track of the document in terms of creation, editing, etc., and makes this information accessible to all. It is a chain of data being distributed across many computers, with a database containing details about each transaction. This record helps in data security and prevents data modification. This technology also helps create big data from multiple sources of small data paving way for creating a well serving artificial intelligence model. Here in this manuscript, we discuss about the usage of blockchain, its current role in machine learning and challenges faced by it

Eye MG 3D Application - A comprehensive ocular anatomy and pathophysiology 3D atlas with real-time true color confocal images to enhance ophthalmology education and e-Counseling

Concepts pertaining to ophthalmology have lots of theoretical frameworks. Neophyte residents and novice surgeons may have to mentally visualize these concepts during the initial days of training. Only a powerful cognitive tool such as a three?dimensional (3D) eyeball model, with real?time TrueColor confocal images (and not animated images or models), can fill in these intellective mental gaps. Giving the users (i.e., residents and students) the power to choose and visualize various parts of the eye, with multiple magnitudes of zoom, is mandatory for optimal e?learning. To make ophthalmic concept learning better, we have developed a 3D app Eye MG 3D (patent pending) comprising ocular anatomy and pathophysiological 3D models, built on an advanced interactive 3D touch interface, by using patient抯 real?time confocal images to serve as a new?age pedagogical tool and e?counseling. According to our knowledge, there are no applications to date that incorporate real?time high?resolution multimodal confocal fundus images and photoreal visuals for interactive and immersive 3D learning.

3D printing ophthalmology related models for enhancing learning through the concept of puzzle assembly - A comprehensive self-learning tactile tool kit

Practical sessions facilitate teaching, critical thinking, and coping skills, especially among medical students and professionals. Currently, in ophthalmology, virtual and augmented reality are employed for surgical training by using three?dimensional (3D) eyeball models. These 3D models when printed can be used not only for surgical training but also in teaching ophthalmic residents and fellows for concept learning through tactile 3D puzzle assembly. 3D printing is perfectly suited for the creation of complex bespoke items in a cost?effective manner, making it ideal for rapid prototyping. Puzzle making, when combined with 3D printing can evolve into a different level of learning in the field of ophthalmology. Though various 3D eyeball models are currently available, complex structures such as the cerebral venous system and the circle of Willis have never been 3D printed and presented as 3D puzzles for assembling and learning. According to our knowledge, this concept of ophthalmic pedagogy has never been reported. In this manuscript, we discuss in detail the 3D models created by us (patent pending), for printing into multiple puzzle pieces for effective tactile learning by cognitive assembling

Utilizing human intelligence in artificial intelligence for detecting glaucomatous fundus images using human-in-the-loop machine learning

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.

Immersive photoreal new-age innovative gameful pedagogy for e-ophthalmology with 3D augmented reality

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.