Jan 2020 – In the article published by the The Ophthalmologist - Ophthalmic Frontiers: AI, four recognized artificial intelligence and deep learning gurus discuss the use of these advanced technologies in eye care – and predict where they may take us next.
We are pleased to share that Dr Daniel Ting, an EyRIS co-founder is one of the selected experts on this feature.
In the feature amongst other aspects, Daniel shared his insights on the main barriers to clinical adoption of AI. Here is an excerpt from the article.
The potential challenges of AI research and clinical adoption in ophthalmology are numerous. First, AI approaches in ocular disease require a large number of images. Data sharing from different centers is an obvious approach to increase the number of input data for network training, however, increasing the number of data elements does not necessarily enhance the performance of a network. For example, adding large amounts of data from healthy subjects will most likely not improve the classification of disease. Moreover, very large datasets for training may increase the likelihood of making spurious connections. When it comes to using retinal images to predict and classify ocular and systemic disease, a clear guideline for the optimal number of cases for training is needed.
Second, when data are to be shared between different centers, regulations and state privacy rules need to be considered. These may differ between countries, and while they are designed to ensure patients’ privacy, they sometimes form barriers for effective research initiatives and patients’ care. Generally, there is an agreement that images and all other patient-related data need to be anonymized and patient’s consent has to be obtained before sharing, if possible. The implementation of the necessary solutions – including data storage, management, and analysis – is time- and cost-intensive. Investing in data-sharing is a difficult decision, because the financial requirements are high, and the benefit is not immediate. Nonetheless, all AI research groups worldwide should continue to collaborate to rectify this barrier, aiming to harness the power of big data and DL to advance the discovery of scientific knowledge.
Third, the decision for data sharing can sometimes be influenced by the fear that competitors may explore novel results first. This competition can even occur within an institution. Indeed, key performance indicators (as defined by funding bodies or universities, including number of publications, impact factor and citation metrics) may represent major hurdles for effective data sharing. On an institutional level the filing of collaboration agreements with other partners is a long and labor-intensive procedure that slows down analysis of shared data. Such periods may even be prolonged when intellectual property issues are to be negotiated. Given that these are usually multiple-institution agreements, time spans of one year or more are common.
Fourth, a large number of images are required in the training set and they need to be well phenotyped for different diseases. The performance of the network will depend on the number of images, the quality of those images, and how representative the data is for the entire spectrum of the disease. In addition, the applicability in clinical practice will depend on the quality of the phenotyping system and the ability of the human graders to follow that system.
Fifth, though the number of images that are available for diseases such as glaucoma, DR and AMD is sufficient to train networks, orphan diseases represent a problem because of the lack of cases. One approach is to create synthetic fundus images that mimic the disease. This is a difficult task and current approaches have proven unsuccessful (14, 15). In addition, it is doubtful that competent authorities would approve an approach where data do not stem from real patients. Nevertheless, generation of synthetic images is an interesting approach that may have potential for future applications.
Sixth, the capabilities of DL should not be construed as competence. What networks can provide is excellent performance in a well-defined task. Networks are able to classify DR and detect risk factors for AMD, but they are not a substitute for a retina specialist. As such, the inclusion of novel technology into DL systems is difficult, because it will require a large number of data with this novel technology. Inclusion of novel technology into network-based classification systems is a long and costly effort.
For the full article, please visit The Ophthalmologist at https://theophthalmologist.com/subspecialties/ophthalmic-frontiers-ai
|About Asst Prof Daniel Ting|
Daniel Ting is a Vitreoretinal Specialist at the Singapore National Eye Center, Assistant Professor of Ophthalmology at Duke-NUS Medical School, Singapore, and Adjunct Professor at the State Key Laboratory of Ophthalmology at Zhongshan Ophthalmic Center in China.
In 2017, Daniel was awarded the highly prestigious US Fulbright Scholar Award, representing Singapore to visit Johns Hopkins University (JHU) School of Medicine and Applied Physics Laboratory to deepen his understanding on the use of artificial intelligence, big data analytics and telemedicine in the field of Ophthalmology.
Apart from artificial intelligence, big data analytics and diabetes, he has also published and currently involved in numerous retina clinical and imaging researches on retinal detachment, age-related macular degeneration. He serves as a reviewer in many high impact journals, including Diabetes Medicine, Diabetes Care, Hypertension, Ophthalmology, American Journal of Ophthalmology, JAMA Ophthalmology, Investigative Ophthalmology and Visual Sciences and Retina. He is also an external grant reviewer for the artificial intelligence grant in Ophthalmology.
|About The Ophthalmologist The Ophthalmologist brings real journalism to the field of ophthalmology. We cover some of the greatest stories by working with the brightest and best in the field, sharing motivations and opinions in the process. We educate, inform, influence and entertain doctors – and provide genuine insight, in part, thanks to a talented team that “gets it.”|
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