Artificial intelligence has made significant advancements in ophthalmology by analyzing medical images and data. AI algorithms can detect eye diseases like diabetic retinopathy and macular degeneration from retinal images, predict disease risk and progression, and provide treatment recommendations to augment doctors. While AI shows promise in improving diagnosis and access to eye care, limitations include potential data and generalization biases that require addressing through responsible development and validation of these new technologies.
Tele-ophthalmology: the new normal in current timesObaidur Rehman
Covers telehealth and telemedicine in general. Tele-ophthalmology development in India. Practice and patterns as defined by concerned authorities. Guidelines as set up Govt of India. Current tele-ophthalmology projects in India
The presentation I have made and uploaded provides you with an in-depth insight into the patterns the strabismus may take following anomalies of extraocular muscles, deformities of the orbital structures,innnervational disturbances.
The author does not assume responsibility or legal liability for any errors in the text or for the misuse or misapplication of material in this work.
No copyright infringement, or plagiarism intended.
Amrit Pokharel
Tele-ophthalmology: the new normal in current timesObaidur Rehman
Covers telehealth and telemedicine in general. Tele-ophthalmology development in India. Practice and patterns as defined by concerned authorities. Guidelines as set up Govt of India. Current tele-ophthalmology projects in India
The presentation I have made and uploaded provides you with an in-depth insight into the patterns the strabismus may take following anomalies of extraocular muscles, deformities of the orbital structures,innnervational disturbances.
The author does not assume responsibility or legal liability for any errors in the text or for the misuse or misapplication of material in this work.
No copyright infringement, or plagiarism intended.
Amrit Pokharel
Optical coherence tomography angiography optovue a very basic lecture detailing the new advancement of dyeless angiography by spectral domain OCT system and SSADA and Motion correction algorithm
La chirurgie correctrice au laser peut se faire directement sur la surface oculaire a l'aide du laser excimer. Cette technique n'implique pas la découpe d'un volet et est une excellente alternative pour les patients avec de faibles myopies ou des cornées fines.
Update knowledge about Muntifocal IOL made by Asaduzzaman
Working as Associate Optometrist in Ispahani Islamia Eye Institute &Hospita, Dhaka 1215
Email:asad.optom92@yaho. com
Optical coherence tomography angiography optovue a very basic lecture detailing the new advancement of dyeless angiography by spectral domain OCT system and SSADA and Motion correction algorithm
La chirurgie correctrice au laser peut se faire directement sur la surface oculaire a l'aide du laser excimer. Cette technique n'implique pas la découpe d'un volet et est une excellente alternative pour les patients avec de faibles myopies ou des cornées fines.
Update knowledge about Muntifocal IOL made by Asaduzzaman
Working as Associate Optometrist in Ispahani Islamia Eye Institute &Hospita, Dhaka 1215
Email:asad.optom92@yaho. com
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.
Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to
screen abnormalities through retinal fundus images by clinicians. However, limited number of
well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a
big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy
severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images
in the training set, we also automatically segment the abnormal patches with an occlusion test,
and model the transformations and deterioration process of DR. Our results can be widely used
for fast diagnosis of DR, medical education and public-level healthcare propagation.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Development of novel BMIP algorithms for human eyes affected with glaucoma an...Premier Publishers
Glaucoma is one of the second driving eye maladies on the planet, if not treated legitimately may prompt lasting visual impairment. There are no particular side effects when the glaucoma disease is considered, especially for this type of eye disease, the effect of which is the vision loss in the human eyes. Because of measuring, the container zone increments, which will result in the vision impairment in the human eyes. Normally exceptionally prepared opthalmogists physically review eye pictures as tedious way. In this unique circumstance, we are attempting to build up some novel calculations for programmed recognition of eyes influenced with glaucoma utilizing picture preparing separating and change strategies and actualize the same on equipment utilizing micro-controller framework. The product that will be created by us could be implanted on the equipment to test the sound and undesirable fundus pictures for the recognition of glaucoma. The calculations that could be created can be actualized wrt the eye pictures in HDL language utilizing Xilinx ISE, MATLAB and MODELSIM, TI based unit or NI based pack (any one) is the equipment apparatus that is considered for execution purposes.
Classification of OCT Images for Detecting Diabetic Retinopathy Disease using...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
CLASSIFICATION OF OCT IMAGES FOR DETECTING DIABETIC RETINOPATHY DISEASE USING...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
In this presentation I share the ideas regarding Radiology and AI relation to each other . Its helps to explore more about radiology . Its key advaantage is that u find a detailed knowledge of Radio Imaging and AI at the same platform . If u want to know about the healthcare and research centers where AI is used you also find the collabration among various AI TECHNOLOGY MANUFACTURERS and RESEARCH CENTERS . If you are doing graduation in AI or RADIOLOGY field , it well define your stream . It enhance the workfield arena for radigraphers and how they will increase their job profiles and clearly differntiate them between Robots and AI because most of our assests thought AI in healthcare leads to robotic work but they are totally wrong in this , they need to understand that AI is just going to decrease the work pressure on them . This will not going to take their jobs and our radio imaging machines are only operated by our medical proffesssionalists i.e. our Radiotechnician . It will reduce the waiting time for patients and it increase the job satisfaction for us. As a Radio Imaging student , I try to clear all the doubts regarding the job misconceptiopns in our mind. We must be prgressive , truthful and honest while our job responsibility. AI will help in faster Image Analysis , it helps in Diagnostic accuracy, it also helps in early detection of disease , as it will pre analysize the data from various modalities and make a 3D view of the image form . It leads to increase the patients trust on our healthcaere providers . It helps in unnecessary excessive radiation to the patients , leads to decrease in the risk of radiation related diseases , like skin erethema , skin cancer and skin infections . Yeah.. you also find some challenges regarding smart radiology but by doing proper cincern in your imaging field you will going to solve all the errors in it . We must try to organize short confrence in which we share our views and plans for future innovations and this presentation is small effort towards it. It will also control the quality of your images . This presentation will help you differntiate between the TELEMEDICINE and PACS . I also share the requireds tools and techniques which are using in radiological field . Some of them are - machine learning, deep learning, natural language processing, computer aided detection, image segmentation, 3D- Imaging analysis, automated repoting , radiomics, generative adversial networks . It will show you how AI effects life of Radiographers and Technicians , by stating : efficiency and productivity, workload management , skill enhancement, career evolution, job satisfaction, and patients care, quality assurance. Here , I also share some case studies where AI ia implemented in Radiology, it includes Cleveland Clinic collabration with Zebra Medical Vision , Stanford Medicine use of ARTERYS and UNIVERSITY OF CALIFORNIA , SAN FRANCISCO [ UCSF] and TEMPU . Whereas some challenges in adopting AI in Radiology.
Detection of Glaucoma using Optic Disk and Incremental Cup Segmentation from ...theijes
Medical researchers, detection of eye disease is very important because it may causes blindness. Glaucoma is one of the diseases that cause blindness. Standard procedure for detection glaucoma is to analysis of optic disk (OD) and cup region in retinal image. In this paper, introduce an automatic OD parameterized technique which is based on segmentation and Incremental Cup segmentation. The incremental cup segmentation method is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is applied to derive a reliable subset of vessel bends called r-bends followed by a local 2-D spline fitting to derive the desired cup boundary. The results are compared with existing methods using different retinal images.
Similar to Artificial Intelligence In Ophthalmology.pptx (20)
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
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Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
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ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
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2. Introduction
Artificial Intelligence (AI), refers to software that
can mimic cognitive functions such as learning
and problem solving.
Accomplishes tasks by processing and recognizing
patterns in large amounts of data.*
To Augment Not
Replace
*Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Surv Ophthalmol.
2019;64(2):233-240. doi:10.1016/j.survophthal.2018.09.002
3. TYPES OF AI
Simple Automated Detectors: identifies the presence or absence of
features
This rule-based algorithm assesses features and ultimately yields an
outcome (e.g. diagnosis) based on the patterns identified
Machine Learning: more advanced form of AI: the input into machine learning
a training dataset
(starts without knowledge and becomes intelligent)
Deep Learning:
(Mimics learning process of the human
brain)
4. Introduction
• Artificial intelligence (AI) has made
significant advancements in the
field of ophthalmology
• Has the potential to revolutionize
various aspects of eye care
5. Artificial Intelligence in Ophthalmology
HOW IT WORKS
Image Analysis
Treatment
Recommendatio
ns
Disease Risk
Prediction:
Data Integration
and Decision
Support:
Research and
Drug Discovery
Dose
Optimization
Akkara JD, Kuriakose A. Role of artificial intelligence and machine learning in ophthalmology. Kerala J
6. AI in Retina
2018
Diabetic Retinopathy (DR)
Retinopathy Of Prematurity (ROP)
Age-related Macular Degeneration (AMD)
7. AI in Diabetic Retinopathy
2018
• AI algorithms
• Analyze retinal images
• Identify signs of diabetic
retinopathy, such as
microaneurysms, hemorrhages,
and exudates.
• Screen large populations more
efficiently
• Enable early diagnosis and
intervention.
Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial
of an autonomous AI-based diagnostic system for detection of
diabetic retinopathy in primary care offices. NPJ Digit Med.
2018;1:39. doi:10.1038/s41746-018-0040-6
8. AI in Age-RelatedMacular Degeneration (AMD)
• AI algorithms can analyze : retinal images and
optical coherence tomography (OCT) scans
• Detects and classify different stages of AMD.
• Assists ophthalmologists in making accurate
diagnoses and monitoring disease progression.
Russakoff DB, Lamin A, Oakley JD, Dubis AM, Sivaprasad S. Deep Learning for Prediction of AMD Progression: A Pilot
Study. Invest Ophthalmol Vis Sci. 2019;60(2):712-722.
9. AI in Age-RelatedMacular Degeneration (AMD)
• i-ROPDL systemcan distinguish features
such as plus disease and is
comparable or better than expert
diagnosis
Brown JM, Campbell JP, Beers A, et al. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep
Convolutional Neural Networks. JAMA Ophthalmol. 2018;136(7):803-810. doi:10.1001/jamaophthalmol.2018.1934
10. AI in Glaucoma
• Glaucoma diagnosis and progression
monitoring
• Algorithms can analyze visual field
tests, optic nerve images, and oct scans
• Aid in early detection and personalized
treatment planning.
• Muhammad et al. developed a deep
learning algorithm that accurately
identifies glaucoma suspects, allowing
for more timely management
Muhammad H, Fuchs TJ, De Cuir N, et al. Hybrid Deep
Learning on Single Wide-field Optical Coherence
tomography Scans Accurately Classifies Glaucoma Suspects. J
11. AI in CATARACT:
The advent of AI can potentially transform the management of cataract in terms of assessment
and monitoring, IOL calculation, intraoperative feedback, and postoperative care.
• Detect and grade cataracts
• Resnet(residual neural network) to identify
referable cataracts
• Adequately guide plans for surgical
intervention
• Anticipate the likelihood of posterior
capsular opacification
• Calculating intraocular lens power(HILL-RBF
Akkara. Role of artificial intelligence and machine learning in
ophthalmology.
Liu X, Jiang J, Zhang K, et al. Localization and diagnosis
12. AI in Ocular Oncology
• Anticipate the course of periocular
reconstruction during surgical treatment of
basal cell carcinoma.
• Predict disease outcomes for choroidal
melanoma.
• Multispectral imaging system for the
detection of ocular surface squamous
neoplasia.
Akkara. Role of artificial intelligence and machine learning in
ophthalmology.
Habibalahi A, Bala C, Allende A, Anwer AG, Goldys EM. Novel
13. AI in Surgical Planning and Guidance
• Assist ophthalmic surgeons
• Provide preoperative planning
• Intraoperative guidance
• Determine optimal surgical approach
• Predict outcomes
• Offer guidance during surgical procedures
like cataract surgery or refractive surgery
Brummen A, Owen J, et al. Artificial intelligence automation of
eyelid and periorbital measurements. Investigative
14. AI in Teleophthalmology
• AI-powered teleophthalmology platforms
• Allows remote diagnosis and monitoring of eye
conditions.
• Patients can capture retinal images or perform
visual field tests at home, which are then
analyzed by AI algorithms.
• This enables access to eye care in underserved
areas and facilitates timely management of
eye diseases.
15. LIMITATIONS
• Data Bias: “garbage in, garbage out”
phenomenon
• Limited Generalization
• Lack of Explainability
• Regulatory and Ethical Considerations
• Cost and Infrastructure
16. Conclusion
• AI has transformative impact
• Algorithms have been designed for image
analysis, disease diagnosis, risk
prediction, treatment recommendations,
and surgical guidance
• May require significant investments in
terms of computational resources,
infrastructure, and training
• Need for responsible development,
validation, and ethical implementation
17. Thank you
Artifi cial I
ntelligenceispoised totransform thefi eld of
ophthalmology, revolutionizing diagnosis, treatment, and
patient care.
Byembracingthistechnology, we can improve outcomes,reduce
costs,and providebettereyecareforall.
Editor's Notes
Potential Applications of Artificial Intelligence in Ophthalmology
if the initial dataset presented to the machine is inadequate, then the predictions generated by the AI tool will be inaccurate. In some situations, output recommendations by AI tools may be simply incorrect.
Erroneous predictions by AI algorithms can bring up the issue of liability for physicians.
By complementing the role of physicians, AI has the potential to significantly improve patient care by increasing efficiency and outcomes as it becomes incorporated into clinical practice in the near future.
