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COVID-19 since its appearance caused serious problems to the health sector due to the increase in infected and deceased people by directly affecting their respiratory system, making it a primordial disease that led all countries to fight this virus, generating that other diseases go to the background such as diabetes mellitus, which is a disease caused by the neglect of people's lifestyles, that has been increasing over time and that has no cure but can be prevented by controlling your blood glucose level, this disease causes diabetic retinopathy in people that with the advance of it can cause loss of sight. In addition, to detect its stage the ophthalmologist relies on his experience, occupying a lot of time and being prone to make mistakes about the patient. In view of this problem, in this article a digital image processing system was performed for the detection of diabetic retinopathy and classified according to the characteristics obtained from the features by analyzing the fundus of the eye automatically and determining the stage in which the patient is. Through the development of this system, it was determined that it works in the best way, visualizing an efficiency of 95.78% in the detection of exudates, and an efficiency of 97.14% in the detection of hemorrhages and blood vessels, resulting in a reliable and safe system to detect diabetic retinopathy early in diabetic patients. © 2023 IEEE.
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With the development of technology, doctors can diagnose and treat many diseases through telemedicine. At present, teleophthalmology is mainly used in screening and diagnosing some ocular diseases, monitoring chronic ocular diseases, as well as teleconsultation. With the increasing demand and application of teleophthalmology, some problems will gradually become prominent, such as insufficient equipment and staff, medical risks, patient acceptance and satisfaction, network security, privacy, and covering of medical insurance. The global pandemic of COViD-19 has unexpectedly brought telemedicine to the forefront of ophthalmic services, and may continue to change the way of ophthalmic diagnosis and treatment. With the development of artificial intelligence technology, the expansion of 5G communication network coverage, the standardized training of primary medical staff, and the introduction of relevant laws and regulations, teleophthalmology will become more improved, universal, and widely applied, so as to provide patients with sustainable medical services of higher quality.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.
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The renin–angiotensin system is major endocrine, paracrine, and autocrine system that has a notorious role in regulating hemodynamics and electrolyte balance, thus controlling blood pressure. Nonetheless, the functions of RAS are multiple and continue to expand, as its receptors, enzymes, and peptides have been identified in different organs/tissues, implying novel local effects of RAS and participation in the pathogenesis of different diseases in multiple organs, including the eyes. Vascularized, fibrous, and nervous structures of the eyes and their fluids express several RAS components including angiotensinogen, prorenin, renin, (P)RR, ACE, Ang II, AT1R, AT2R, ACE2, Ang 1-7, and Mas. Additionally, abundant RAS hormones are present in intraocular tissues implicating local production. Ocular RAS functions seem to be diverse;first, RAS is proposed to modulate aqueous humor biosynthesis and drainage, consequently regulating IOP. Exacerbation of ACE/ Ang II/ AT1R actions is observed in glaucoma, and blockage of this axis with ACEIs and ARBs is a feature that can be explored to reduce IOP and treat glaucoma. Second, impairment of RAS with exacerbation of ACE/ Ang II/ AT1R and depletion of ACE2/ Ang 1-7/ Mas culminate in an angiogenic, inflammatory, proliferative, and oxidative phenotype, which is tightly related to retinal vascular disorders such as diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. Indeed, treatments blocking ACE/ Ang II/ AT1R or enhancing ACE2/ Ang 1-7/ Mas are reported to be beneficial to prevent or restrain the progression of these diseases. Finally, clinical manifestations of COVID-19 include ocular disorders such as conjunctivitis, dry eyes, eye pain, discharge, and blurred vision. ACE2 expression in ocular tissues may define ophthalmic susceptibility to SARS-CoV-2 infection. Moreover, SARS-CoV-2 entry mechanisms may cause depletion of ACE2 biological effects, which can contribute to RAS dysregulation and the ocular disorders observed in COVID-19. This chapter focuses on reviewing the components and functions of the ocular RAS, its implications for eye homeostasis, and the potential of RAS as a therapeutic target to treat ocular disorders, including glaucoma, diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. © 2023 Elsevier Inc. All rights reserved.
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This report describes a case of a newly diagnosed 49-year-old HIV patient, who presented with decreased visual acuity and retinal lesions characterized by ischemia at the level of the deep retinal capillary plexus, documented with optical coherence tomography (OCT), OCT angiography, fluorescein angiography, and visual fields testing. These lesions closely resembled the morphologic and clinical characteristics of late paracentral acute middle maculopathy. The presence of these lesions suggests that HIV microangiopathy can potentially affect both superficial and deep retinal capillary plexuses.
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Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson's Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer's disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.
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The number of people with diabetes globally, is rising at an alarming rate. South Asia is one of the hot spots of the diabetes epidemic. In India alone, there are over 74 million people with diabetes today. Unfortunately, 70% of the doctors in India practice in urban areas while 70% of India's population lives in rural areas. This mismatch between the availability of health care professionals and the rapid spread of diabetes in rural areas, provides an opportunity to use technology to deliver the diabetes care to remote rural areas. The first part of this presentation will talk about a model of successful delivery of diabetes health care in rural India. The Chunampet Rural Diabetes Program was carried out in a group of 42 villages in Kancheepuram District in Tamilnadu. Using a Mobile van, a population of 27,014 individuals (86.5% of the adult population) were screened for diabetes. All those detected with diabetes were offered a follow up care at a rural diabetes centre which was set up during the project. The results were very impressive and led to good improvement in A1c levels using low cost generic drugs. The second use of technology was during the COVID - 19 pandemic and the lock down which was enforced in India and many other countries. Thankfully, Telemedicine was also legalized in India at that time. Using technology, a system was created whereby the doctor and the patient stayed at home but blood tests were arranged at home for the patient.With the results, teleconsultation was done by doctors using the Electronic Medical Records which were made available on their mobile phones. Thus, despite the lockdown, patients managed to get their tests and diabetes consultations done remotely. The third use of technology is through our network of diabetes clinics across India. Even at centres where there was no ophthalmologist, retinal photographs were obtained using a lowcost retinal camera and were uploaded for centralized diabetic retinopathy grading unit where the images were read by trained retina specialists. The eye reports were sent back to the peripheral clinics in real time. Over one year period, 25,316 individuals with diabetes could have their eyes screened for diabetic retinopathy. Only 11.4 % needed referral to an ophthalmologist for further management. Finally, the use of mobile Apps has revolutionized diabetes treatment. Recently, we have developed three diabetes related tools. 'DIA' - an AI powered chatbot to assist people through automated digital conversations, 'DIALA' - a patientfriendly mobile app and 'DIANA' - a healthcare application for precision diabetes care. The details of these three tools are briefly described below : DIA : The Conversational AI Virtual Assistant 'DIA' can interact in English with its unique conversational AI technology and intuitive interface, it has proved to be a useful solution for patients, providing complex dialogues, with quick response time and offers comprehensive solutions for patients with diabetes. DIA's uses range from scheduling appointments and reminders for visits, lab tests and teleconsultation, to addressing enquiries on available medicines, treatments, and facilities.During an emergency, health crisis or in pandemic situations, it connects with caregivers and patients to take proper action as per the seriousness of their conditions. Further, it shares notifications, updates patient engagement and special offers. In addition to this, DIA can assist patients through reminders on their medicine refill via WhatsApp or SMS notifications and even facilitate purchase and tracking of medicine orders. DIALA : 'DIALA' is a DIAbetes Lifestyle Assistant Mobile Application. This app helps deliver superior and positive patient outcomes with weight tracking, step counts, diet plan adjustment, prescription refilling, availing reports of tests done, glucose monitoring data, scheduling appointments and sends reminders. It can help to monitor one's health and manage diabetes effectively. It is currently available in Android. DIANA : An advanced machine learning tool DIANA (DIAbetes Novel subgroup Assessment) is used to classify individuals with newly detected type 2 diabetes into specific subgroups such as insulin deficient or insulin resistance forms. This tool also gives the estimates of the risk for developing diabetes complications like eye or kidney disease. This machine learning approach has been developed based on published real world clinical data and will help the clinician offer individualized care for people with diabetes. In conclusion, judicious use of technology can help to bridge the socioeconomic and geographical challenges in delivering diabetes health care in developing countries.
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Relevance. The coronavirus rush, which has appeared since December 2019, has an impact on economic, medical, and social development in all countries of the world. There are still no standard diagnostic and therapeutic plans aimed at limiting this infection. Purpose. To determine the therapeutic role of tracheostomy with patients with coronavirus infection. Material. The prospective study of 100 patients with coronavirus infection was carried out on the basis of State Budgetary Healthcare Institution "Interdistrict Multidisciplinary Hospital" in Nartkala city. When patients were admitted to the clinic, they were randomized into 2 groups depending on the therapy: the first group (comparison, n=50) - patients received traditional therapy in intensive care;the second (main, n=50) group - patients underwent tracheostomy in addition to standard therapy. The average age was 56.2+/-4.8 years. The women's average age was 55 years (55.0%), and the men's - 45 years (45.0%). Methods. Determination of the syndrome of endogenous intoxication, taxation of lipid peroxidation intensification. The local microcirculation was investigated by the apparatus LAKK-02. The activity of the coagulation-lytic blood system was found by thrombelastograph TEG 5000. Results. Early COVID-19 infection demonstrates signs of intoxication, oxidative depression, phospholipase activity, microcirculatory and hemostatic disorders. These changes were the cause of the development of life-threatening complications (neurological, pulmonary, cardiac, etc.). They were associated with the type of treatment. Traditional treatment and the use of mask oxygen turned out to be ineffective, since the homeostasis system disorders remained throughout the investigation period. The inclusion of tracheostomy in traditional therapy makes it possible to quickly improve the course of pathology as it purposefully affects the pathogenetic links of the homeostasis system. Conclusion. The use of tracheostomy in the scheme of standard treatment of coronavirus infection allows improving the effectiveness of general therapy of COVID-19 patients, especially with severe forms.Copyright © 2023, Codon Publications. All rights reserved.
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Developing countries like Nepal face challenges in accessing health services due to sparse distribution in communities, difficult geographic terrain, limited transportation, poverty, and lack of health human expertise in rural areas. The COVID-19 pandemic added woes to the wound. To address this gap, the Hospital for Children, Eye, ENT, and Rehabilitation Services adopted an innovative approach to remote rural patient care using telehealth and artificial intelligence in close coordination with IT professionals and healthcare professionals. We developed a deep learning-based disease prediction model that incorporates telemedicine with AI for screening and diagnosing Eye and ENT diseases using nonspecialist health workers. Deep learning-based disease prediction models in Diabetic Retinopathy (DR) and Glaucoma added quality specialized services to telehealth. This paper presents the adoption of digital innovations and the incorporation of telehealth to tackle various diseases. To predict DR, 61,458 colorful retinal photographs from fundus photography and 1500 for Glaucoma were used. To reduce the biases, EyePACS data sets were also incorporated. Inception V3 transfer learning model was used for DR and employed DenseNet architecture for Glaucoma. An accuracy of more than 90 %in both models was achieved. Accurate specialized diagnosis, better medical care, patient monitoring, limited specialized hospital visits, and easier with shorter wait times are now possible. In the future, this successful model can be replicated nationally and in other developing countries. © 2022 IEEE.
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Purpose: To study patient follow-up after they engage in a teleretinal screening program and to understand potential barriers to care. Methods: This was a retrospective analysis and a prospective study of telephone-based patient interviews of outpatients screened for diabetic retinopathy (DR) through a teleretinal referral system. Results: Of 2761 patients screened through a teleretinal referral program, 123 (4.5%) had moderate nonproliferative DR (NPDR), 83 (3.0%) had severe NPDR, and 31 (1.1%) had proliferative DR. Of the 114 patients with severe NPDR or worse, 67 (58.8%) saw an ophthalmologist within 3 months of referral. Eighty percent of interviewed patients reported they were not aware of the need for follow-up eye appointments. Conclusions: Of patients with severe retinopathy or worse, 58.8% presented for in-person evaluation and treatment within 3 months of screening. Although this result was negatively affected by factors related to the COVID-19 pandemic, key elements of patient education and improved referral strategies to facilitate in-person treatment are essential to improving follow-up after patients engage in telescreening.
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Introduction The objective of this study was to identify barriers that affect adherence to the management of diabetic retinopathy (DR) in an urban ophthalmology clinic. Patient beliefs regarding diabetic eye care, transportation to the eye clinic, the COVID-19 pandemic, and treatment with panretinal photocoagulation (PRP) or anti-vascular endothelial growth factor (anti-VEGF) injections were investigated. Materials and methods The original Compliance with Annual Diabetic Eye Exams Survey (CADEES) included 44 statements designed with a 5-point Likert scale to assess patients' beliefs and understanding of their eye health and the importance of diabetic eye examinations. This survey was modified to include additional statements regarding the COVID-19 pandemic and free-response questions about transportation barriers and patients' subjective experiences with PRP or anti-VEGF injections. A total of 365 patients with a diagnosis of any stage of DR from SLUCare Ophthalmology were identified as potential participants to complete the telephone survey. Patients were classified as non-adherent if they did not have a dilated eye examination within the past year, missed a scheduled follow-up appointment for DR care within the past year, or missed an appointment for anti-VEGF injections or PRP. The mean Likert scores for each CADEES statement were compared between the adherent and non-adherent groups using independent samples t-tests. Demographics and clinical indicators were also reported and compared between the two groups. Results Out of 365 patients, 68 completed the modified CADEES. Twenty-nine patients were adherent, and 39 patients were non-adherent. Results from six of the 54 CADEES statements were significantly different between the adherent and non-adherent groups. These statements addressed patients' perception of their eye health, self-confidence in making an eye appointment, knowing someone with diabetic eye complications, self-confidence in controlling blood sugar, ability to use public transportation during the COVID-19 pandemic, and prioritizing eye health during the pandemic. There were no significant differences in clinical indicators or demographics between the adherent and non-adherent groups. Of the participants, 39.7% offered reasons for why transportation to the eye clinic was difficult. Patients suggested three novel reasons for missing eye appointments that were not specifically addressed in the CADEES. Fourteen unique barriers were reported for non-adherence with PRP or anti-VEGF injections. Conclusions The CADEES is a thorough tool for evaluating social barriers impacting adherence with DR appointments in an urban ophthalmology clinic. The survey did not identify any clinical or demographic risk factors for non-adherence in this patient population. Decreased patient self-efficacy may lead to non-adherence with the management of DR. The COVID-19 pandemic impacted the adherence of a small percentage of patients.
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Background: Diabetes mellitus (DM) represents one of the most common metabolic diseases in the world, with rising prevalence in recent decades. Most cases are generally classified into two major pathophysiological categories: type 1 diabetes mellitus (DM1), which progresses with absolute insulin deficiency and can be identified by genetic and pancreatic islet autoimmunity markers, and type 2 diabetes mellitus (DM2), which is the most prevalent form and involves a combination of resistance to the action of insulin with an insufficient compensatory response of insulin secretion. In the last two decades, in parallel with the increase in childhood obesity, there has also been an increase in the incidence of DM2 in young people in some populations. Other forms of diabetes may affect children and adolescents, such as monogenic diabetes (neonatal diabetes, MODY – maturity onset diabetes of the young, mitochondrial diabetes, and lipoatrophic diabetes), diabetes secondary to other pancreatic diseases, endocrinopathies, infections and cytotoxic drugs, and diabetes related to certain genetic syndromes, which may involve different treatments and prognoses. DM1 is considered an immuno-mediated disease that develops as a result of gradual destruction of insulin-producing pancreatic beta cells that eventually results in their total loss and complete dependence on exogenous insulin. Clinical presentation can occur at any age, but most patients will be diagnosed before the age of 30 years
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Artificial intelligence (AI), deep learning (DL), and neural networks (NN), though these words sound flashy and may leave you perplexed, represent powerful technologies that have the capabilities to transform the world. It is just now emerging how valuable these machine learning-based techniques are and how they can solve many real-world problems ranging from fraud detection, resource management to driver-less cars.One such field where the application of AI systems is progressively growing is in medical diagnosis. A lot of research is going on to enhance computer-Aided diagnosis and detection of diseases. Recent world events have tested the healthcare systems all around the world. Suppose we have sophisticated deep learning systems (DLS) that could help in faster and efficient disease detection and diagnosis;how beneficial it would be to assist both medical professionals and patients.This study explores how AI and machine learning techniques could be used for disease detection, giving COVID-19 and Diabetic Retinopathy detection examples. We present two deep learning (DL) models, one to detect COVID-19 from chest x-ray image scans and the other to detect Diabetic Retinopathy at various stages of the disease from retinal fundus images. With reasonably high accuracy, >95% for the COVID-19 detection model and >80% for the Diabetic Retinopathy detection model, these results highlight AI and deep learning potential to assist general practitioners. © 2022 IEEE.
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Abstract Background: Follow-up adherence with in-person care is critical for achieving improved clinical outcomes in telemedicine screening programs. We sought to quantify the impact of the COVID-19 pandemic upon follow-up adherence and factors associated with follow-up adherence after teleophthalmology for diabetic eye screening. Methods: We retrospectively reviewed medical records of adults screened in a clinical teleophthalmology program at urban and rural primary care clinics between May 2015 and December 2020. We defined follow-up adherence as medical record documentation of an in-person eye exam within 1 year among patients referred for further care. Regression models were used to identify factors associated with follow-up adherence. Results: Among 948 patients, 925 (97.6%) had health insurance and 170 (17.9%) were referred for follow-up. Follow-up adherence declined from 62.7% (n = 52) prepandemic to 46.0% (n = 40) during the pandemic (p = 0.04). There was a significant decline in follow-up adherence among patients from rural (p < 0.001), but not urban (p = 0.72) primary care clinics. Higher median household income (odds ratio [OR] 1.68, 95% confidence interval [CI]: 1.19-2.36) and obtaining care from an urban clinic (OR 5.29, 95% CI: 2.09-13.43) were associated with greater likelihood of follow-up during the pandemic. Discussion: Follow-up adherence remains limited after teleophthalmology screening even in a highly insured patient population, with a further decline observed during the COVID-19 pandemic. Our results suggest that rural patients and those with lower socioeconomic status experienced greater barriers to follow-up eye care during the COVID-19 pandemic. Conclusions: Addressing barriers to in-person follow-up care is needed to effectively improve clinical outcomes after teleophthalmology screening.
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OP042 / #448 Topic:AS06-Informatics in the Service of Medicine;Telemedicine, Software and other Technolog... B ORAL PRESENTATIONS SESSION 3 b REDUCTION IN DIABETES-RELATED HOSPITALIZATION RATES AFTER REAL-TIME CONTINUOUS GLUCOSE MONITO... I K. Hannah, P. Nemlekar, G. Norman i I Dexcom, Health Economics And Outcomes Research, Global Access, San Diego, United States of America i B Background and Aims: b Inadequate glycemic control in patients with diabetes can result in diabetes-related hospitalizations. Finally, telehealth services provided opportunities even to initiate diabetes treatment in those newly diagnosed and to implement changes in diabetes management for those with established diabetes, including the implementation of advanced diabetes technologies. OP012 / #730 Topic:AS04-Clinical Decision Support Systems/Advisors B VIRTUAL ORAL PRESENTATIONS SESSION 1 b ALGORITHM-DRIVEN BASAL-BOLUS THERAPY IN HOSPITALIZED PATIENTS WITH TYPE 2 DIABETES: IMPLICATI... I D. Hochfellner SP 1 sp , P. Baumann SP 1 sp , P. Beck SP 2 sp , J. Mader SP 1 sp i I SP 1 sp Medical University of Graz, Division Of Endocrinology And Diabetology, Graz, Austria, SP 2 sp decide Clinical Software GmbH, -, Graz, Austria i B Background and Aims: b Diabetes therapy in hospitalized patients with type 2 diabetes (T2D) often fails to improve glycemic control during inpatient stay and beyond. Data regarding gender specific differences in diabetes outcomes and gender-related risk factors would be key to devise customized diabetes management plans to improve diabetes outcomes and quality of life for people with diabetes. [Extracted from the article]
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Insulin degludec/insulin aspart Low fasting glucose-to-estimated average glucose ratio was associated with superior response to insulin degludec/aspart compared with basal insulin in patients with type 2 diabetes, Jang 85-93. Type 1 diabetes mellitus Impact of flash glucose monitoring on glycemic control varies with the age and residual -cell function of patients with type 1 diabetes mellitus, Zhang 552-559. Diabetes mellitus type 2 Current status of oral antidiabetic drug prescribing patterns based on the body mass index for Japanese type 2 diabetes mellitus patients and yearly changes in diabetologists' prescribing patterns from 2002 to 2019 (JDDM61), Yagi 65-73. 2A protease Immunohistochemical detection of enteroviruses in pancreatic tissues of patients with type 1 diabetes using a polyclonal antibody against 2A protease of Coxsackievirus, Jimbo 435-442. Type 2 diabetes mellitus Bariatric surgery versus medical treatment in mildly obese patients with type 2 diabetes mellitus in Japan: Propensity score-matched analysis on real-world data, Seki 74-84. [Extracted from the article]
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In the field of medical science, the reliability of the results produced by deep learning classifiers on disease diagnosis plays a crucial role. The reliability of the classifier substantially reduces by the presence of adversarial examples. The adversarial examples mislead the classifiers to give wrong prediction with equal or more confidence than the actual prediction. The adversarial attacks in the black box type is done by creating a pseudo model that resembles the target model. From the pseudo model, the attack is created and is transferred to the target model. In this work, the Fast Gradient Sign Method and its variants Momentum Iterative Fast Gradient Sign Method, Projected Gradient Descent and Basic Iterative Method are used to create adversarial examples on a target VGG-16 model. The datasets used are Diabetic Retinopathy 2015 Data Colored Resized and SARS-CoV-2 CT Scan Dataset. The experimentation revealed that the transferability of attack is true for the above described attack methods on a VGG-16 model. Also, the Projected Gradient Descent attack provides a higher success in attack in comparison with the other methods experimented in this work. © 2022 IEEE.
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(1) Background: Diabetic retinopathy (DR) is a complication of diabetes mellitus (DM), screening programs of which have been affected by the COVID-19 pandemic. The aim of the present study was to determine the impact of the COVID-19 pandemic on the screening of diabetes patients in our healthcare area (HCA). (2) Methods: We carried out a retrospective study of patients with DM who had attended the DR screening program between January 2015 and June 2022. We studied attendance, DM metabolic control and DR incidence. (3) Results: Screening for DR decreased in the first few months of the pandemic. The incidence of mild and moderate DR remained stable throughout the study, and we observed little increase in severe DR, proliferative DR and neovascular glaucoma during 2021 and 2022. (4) Conclusions: The current study shows that during the COVID-19 pandemic, screening program attendance decreased during the year 2020, which then recovered in 2021. Regarding the most severe forms of DR, a slight increase in cases was observed, beginning in the year 2021. Nevertheless, we aimed to improve the telemedicine systems, since the conditions of a significant proportion of the studied patients worsened during the pandemic; these patients are likely those who were already poorly monitored.
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Both current and new patients at Sankara Nethralaya Eye Hospital can use free community-based teleophthalmology services as well as paid online services, even if they do not have personal internet access. The first eye hospital in India to use teleophthalmology to offer primary eye treatment to patients in remote communities was Chennai's Sankara Nethralaya Eye Hospital in 2003. This free service provides thorough eye exams as well as screening for cataracts and diabetic retinopathy utilizing a satellite link put on the roof of a mobile eye care van. It quickly became apparent that more people not simply those in rural areas would need teleophthalmology services with the COVID-19 pandemic's arrival in 2020. The Indian government's practice guidelines for telemedicine, which it released at the start of the COVID-19 pandemic offered a framework for the control and expansion of teleconsultation services in the nation. Sankara Nethralaya set up three new teleophthalmology access points for new patients, current patients, and patients who do not have access to the internet but can visit an optical store in response to this and in addition to its current service in rural areas. A patient can consult an ophthalmologist directly from home using this paradigm, which uses not many resources. An electronic medical records system, teleconsultation capabilities (audio/video calling), and a payment portal are necessities for the hospital. After their eligibility has been verified at the hospital, clients who are unable to pay can receive free teleophthalmology services. Patients utilizing this model must have a smartphone and an internet connection (mobile internet or otherwise). Patients without smartphones are unable to share medical records or photographs, but they can still seek advise from doctors by making audio calls and sending short messages (SMS), as well as visiting an optical store that is affiliated with the hospital, if one is close. Teleconsultations are useful for pre-operative counseling, second opinions, evaluating uploaded patient records, and monitoring post-operative patients. Orthoptic, contact lens, poor vision, rehabilitation, and genetic counseling services can all be provided via teleconsultations. The quality management staff will be able to provide better services by gathering patient input after each teleconsultation and responding quickly to complaints.