Displaying all 5 publications

Abstract:
Sort:
  1. Sidibé D, Sankar S, Lemaître G, Rastgoo M, Massich J, Cheung CY, et al.
    Comput Methods Programs Biomed, 2017 Feb;139:109-117.
    PMID: 28187882 DOI: 10.1016/j.cmpb.2016.11.001
    This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works.
  2. Chaikitmongkol V, Sagong M, Lai TYY, Tan GSW, Ngah NF, Ohji M, et al.
    Asia Pac J Ophthalmol (Phila), 2021 Nov 24;10(6):507-518.
    PMID: 34839342 DOI: 10.1097/APO.0000000000000445
    PURPOSE: Review and provide consensus recommendations on use of treat-and-extend (T&E) regimens for neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) management with relevance for clinicians in the Asia-Pacific region.

    METHODS: A systematic search of MEDLINE, EMBASE, and Cochrane databases, and abstract databases of the Asia-Pacific Vitreo-retina Society, European Society of Retina Specialists, American Academy of Ophthalmology, and Controversies in Ophthalmology: Asia-Australia congresses, was conducted to assess evidence for T&E regimens in nAMD. Only studies with ≥100 study eyes were included. An expert panel reviewed the results and key factors potentially influencing the use of T&E regimens in nAMD and PCV, and subsequently formed consensus recommendations for their application in the Asia-Pacific region.

    RESULTS: Twenty-seven studies were included. Studies demonstrated that T&E regimens with aflibercept, ranibizumab, or bevacizumab in nAMD, and with aflibercept in PCV, were efficacious and safe. The recommendation for T&E is, after ≥3 consecutive monthly loading doses, treatment intervals can be extended by 2 to 4 weeks up to 12 to 16 weeks. When disease activity recurs, the recommendation is to reinject and shorten intervals by 2 to 4 weeks until fluid resolution, after which treatment intervals can again be extended. Intraretinal fluid should be treated until resolved; however, persistent minimal subretinal fluid after consecutive treatments may be tolerated with treatment intervals maintained or extended if the clinical condition is stable.

    CONCLUSIONS: T&E regimens are efficacious and safe for nAMD and PCV, can reduce the number of visits, and minimize the overall burden for clinicians and patients.

  3. Sheng B, Guan Z, Lim LL, Jiang Z, Mathioudakis N, Li J, et al.
    Sci Bull (Beijing), 2024 Jan 04.
    PMID: 38220476 DOI: 10.1016/j.scib.2024.01.004
  4. Gunasekeran DV, Zheng F, Lim GYS, Chong CCY, Zhang S, Ng WY, et al.
    Front Med (Lausanne), 2022;9:875242.
    PMID: 36314006 DOI: 10.3389/fmed.2022.875242
    BACKGROUND: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.

    METHODS: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.

    RESULTS: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83.

    CONCLUSION: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

  5. Li J, Guan Z, Wang J, Cheung CY, Zheng Y, Lim LL, et al.
    Nat Med, 2024 Jul 19.
    PMID: 39030266 DOI: 10.1038/s41591-024-03139-8
    Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P 
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links