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1 . Early detection and diagnosis , AI image analysis algorithms can detect early signs of eye disease such as diabetic retinopathy and age-related macula degeneration as well as identify risks of glaucoma . These are 3 of the most common causes of irreversible vision loss . Early detection and treatment can prevent or reduce visual function loss . Hence , it results in better patient outcomes .
2 . Personalised management . AI technology can analyse retinal images and provide clinicians with customised management recommendations for each patient , based on the eye condition and severity of the disease .
3 . Improved accessibility and efficiency . AI analyses can process large volumes of retinal images rapidly , reducing the time and cost required for manual evaluations by trained specialists . It is an effective tool for large-scale screening , providing access to a greater cross-section of the community .
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The Artificial Intelligence ( AI ) Retinal Image Analysis works by taking retinal images and uploading them to an AI module for analysis whereby a professional report will be generated in 3 minutes . |
Over the past decade , medical technology has taken great strides , opening new possibilities for patients . With increased accessibility , early detection and superior treatment options are now within reach , greatly enhancing the chances of successful recovery . Gleneagles Hospital Kuala Lumpur recognises the importance of regularly evaluating new treatments , services , technologies , and data to provide value-based care for patients . It has been successfully integrating AI technology into ophthalmology , cardiology , and radiology departments , enabling seamless service and increased accuracy in diagnosis . In eye care alone , AI screened over ��� patients in the first two months of its adoption , accurately identifying five cases of diabetic retinopathy for immediate treatment . The Artificial Intelligence ( AI ) �etinal Image Analysis works by taking retinal images and uploading them to an AI module for analysis whereby a professional report will be generated in 3 minutes .
The AI �etinal Image Analysis provides several advantages ; namely , capturing retinal images in just 1 minute , generating health assessment report in 3 minutes , identifying 9 most common chronic disease risks , has over 95 % accuracy in identifying retinal abnormalities , over 10 million images available for further AI training and deep learning and the ability to detect 35 common retina-related diseases .
With the continued advancement of deep machine learning from millions of images and clinical data correlations , current AI �etinal Image Analysis technology has made significant progress in the way certain eye diseases are diagnosed , leading to earlier
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The key differences between AI �etinal Image Analyses and conventional image analyses are :
1 . Accuracy . Extensive AI learning from millions of retinal images correlated with clinical data sets has resulted in a system that is highly sensitive and specific for detecting retinal diseases . The accuracy of AI image analyses has been tested and compared favourably against highly experienced retinal specialists .
2 . Consistency and objectivity . AI analyses have been proven to provide consistent diagnoses and staging of disease severity against different retinal specialists and over time . AI algorithms are not in�uenced by subjective interpretations or bias , leading to more consistent diagnoses and grading of disease severity compared to conventional interpretations by human observers .
� . Speed and efficiency . Automated AI image analyses can process retinal images much faster than humans , which reduces the time and cost required for manual screening of conventional images by ophthalmologists . Large volumes of retinal images can be analysed rapidly , allowing a higher and faster through-put of patients undergoing health screening .
The AI �etinal Image Analysis is designed for health screening purposes only and it shall not be considered as primary and conclusive diagnosis of a current medical condition or treatment . �esults derived from the Analysis do not constitute medical advice and are subject to verification � confirmation by a �ualified Specialist Doctor .
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