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  1. Article ; Online: Recent evidence of economic evaluation of artificial intelligence in ophthalmology.

    Ruamviboonsuk, Paisan / Ruamviboonsuk, Varis / Tiwari, Richa

    Current opinion in ophthalmology

    2023  Volume 34, Issue 5, Page(s) 449–458

    Abstract: Purpose of review: Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ... ...

    Abstract Purpose of review: Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ophthalmology are reviewed.
    Recent findings: Majority of recent HEEs of artificial intelligence in ophthalmology were for diabetic retinopathy screening. Two models, one conducted in the rural USA (5-year period) and another in China (35-year period), found artificial intelligence to be more cost-effective than without screening for diabetic retinopathy. Two additional models, which compared artificial intelligence with human screeners in Brazil and Thailand for the lifetime of patients, found artificial intelligence to be more expensive from a healthcare system perspective. In the Thailand analysis, however, artificial intelligence was less expensive when opportunity loss from blindness was included. An artificial intelligence model for screening retinopathy of prematurity was cost-effective in the USA. A model for screening age-related macular degeneration in Japan and another for primary angle close in China did not find artificial intelligence to be cost-effective, compared with no screening. The costs of artificial intelligence varied widely in these models.
    Summary: Like other medical fields, there is limited evidence in assessing the value of artificial intelligence in ophthalmology and more appropriate HEE models are needed.
    MeSH term(s) Infant, Newborn ; Humans ; Ophthalmology ; Artificial Intelligence ; Diabetic Retinopathy/diagnosis ; Cost-Benefit Analysis ; Delivery of Health Care
    Language English
    Publishing date 2023-07-17
    Publishing country United States
    Document type Review ; Journal Article
    ZDB-ID 1049383-9
    ISSN 1531-7021 ; 1040-8738
    ISSN (online) 1531-7021
    ISSN 1040-8738
    DOI 10.1097/ICU.0000000000000987
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3047: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Single-use versus reuse of instruments in ophthalmic surgery.

    Raman, Rajiv / Rao, Chetan / Ruamviboonsuk, Paisan / Huang, Suber / Sharma, Tarun

    Eye (London, England)

    2023  Volume 37, Issue 14, Page(s) 2839–2840

    MeSH term(s) Humans ; Ophthalmology ; Surgical Instruments
    Language English
    Publishing date 2023-02-08
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 91001-6
    ISSN 1476-5454 ; 0950-222X
    ISSN (online) 1476-5454
    ISSN 0950-222X
    DOI 10.1038/s41433-023-02431-0
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    In stock of ZB MED Cologne/Königswinter

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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 603: Show issues

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  3. Article: Prospective studies on artificial intelligence (AI)-based diabetic retinopathy screening.

    Nanegrungsunk, Onnisa / Ruamviboonsuk, Paisan / Grzybowski, Andrzej

    Annals of translational medicine

    2022  Volume 10, Issue 24, Page(s) 1297

    Language English
    Publishing date 2022-12-27
    Publishing country China
    Document type Editorial ; Comment
    ZDB-ID 2893931-1
    ISSN 2305-5847 ; 2305-5839
    ISSN (online) 2305-5847
    ISSN 2305-5839
    DOI 10.21037/atm-2022-71
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  4. Article: Artificial Intelligence for Diabetic Retinopathy Screening Using Color Retinal Photographs: From Development to Deployment.

    Grzybowski, Andrzej / Singhanetr, Panisa / Nanegrungsunk, Onnisa / Ruamviboonsuk, Paisan

    Ophthalmology and therapy

    2023  Volume 12, Issue 3, Page(s) 1419–1437

    Abstract: Diabetic retinopathy (DR), a leading cause of preventable blindness, is expected to remain a growing health burden worldwide. Screening to detect early sight-threatening lesions of DR can reduce the burden of vision loss; nevertheless, the process ... ...

    Abstract Diabetic retinopathy (DR), a leading cause of preventable blindness, is expected to remain a growing health burden worldwide. Screening to detect early sight-threatening lesions of DR can reduce the burden of vision loss; nevertheless, the process requires intensive manual labor and extensive resources to accommodate the increasing number of patients with diabetes. Artificial intelligence (AI) has been shown to be an effective tool which can potentially lower the burden of screening DR and vision loss. In this article, we review the use of AI for DR screening on color retinal photographs in different phases of application, ranging from development to deployment. Early studies of machine learning (ML)-based algorithms using feature extraction to detect DR achieved a high sensitivity but relatively lower specificity. Robust sensitivity and specificity were achieved with the application of deep learning (DL), although ML is still used in some tasks. Public datasets were utilized in retrospective validations of the developmental phases in most algorithms, which require a large number of photographs. Large prospective clinical validation studies led to the approval of DL for autonomous screening of DR although the semi-autonomous approach may be preferable in some real-world settings. There have been few reports on real-world implementations of DL for DR screening. It is possible that AI may improve some real-world indicators for eye care in DR, such as increased screening uptake and referral adherence, but this has not been proven. The challenges in deployment may include workflow issues, such as mydriasis to lower ungradable cases; technical issues, such as integration into electronic health record systems and integration into existing camera systems; ethical issues, such as data privacy and security; acceptance of personnel and patients; and health-economic issues, such as the need to conduct health economic evaluations of using AI in the context of the country. The deployment of AI for DR screening should follow the governance model for AI in healthcare which outlines four main components: fairness, transparency, trustworthiness, and accountability.
    Language English
    Publishing date 2023-03-02
    Publishing country England
    Document type Journal Article ; Review
    ISSN 2193-8245
    ISSN 2193-8245
    DOI 10.1007/s40123-023-00691-3
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  5. Article: Color vision restrictions for medical school admission: a discussion on regulations in ASEAN countries compared to countries across the world.

    Tan, Ting Fang / Grzybowski, Andrzej / Ruamviboonsuk, Paisan / Tan, Anna C S

    International journal of retina and vitreous

    2023  Volume 9, Issue 1, Page(s) 5

    Abstract: Color vision deficiency impairs one's ability to perceive and discriminate colors. Color-deficient individuals may face discrimination in various occupations, particularly in medical school admissions. This discussion seeks to compare the existing color ... ...

    Abstract Color vision deficiency impairs one's ability to perceive and discriminate colors. Color-deficient individuals may face discrimination in various occupations, particularly in medical school admissions. This discussion seeks to compare the existing color vision requirements for entry to medical school in Southeast Asian countries as compared to countries across the world. Following this, we explore the published evidence in this field, to provide recommendations for future guidelines that will maximize the occupational opportunities for color-deficient individuals.
    Language English
    Publishing date 2023-01-30
    Publishing country England
    Document type Letter
    ZDB-ID 2836254-8
    ISSN 2056-9920
    ISSN 2056-9920
    DOI 10.1186/s40942-023-00441-4
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  6. Article: Influence of Different Types of Retinal Cameras on the Performance of Deep Learning Algorithms in Diabetic Retinopathy Screening.

    Srinivasan, Ramyaa / Surya, Janani / Ruamviboonsuk, Paisan / Chotcomwongse, Peranut / Raman, Rajiv

    Life (Basel, Switzerland)

    2022  Volume 12, Issue 10

    Abstract: Background: ...

    Abstract Background:
    Language English
    Publishing date 2022-10-15
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662250-6
    ISSN 2075-1729
    ISSN 2075-1729
    DOI 10.3390/life12101610
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  7. Article ; Online: Microbial contamination of multiple-dose preservative-free hospital ophthalmic preparations in a tertiary care hospital.

    Chantra, Somporn / Hathaisaard, Pinyada / Grzybowski, Andrzej / Ruamviboonsuk, Paisan

    Advances in ophthalmology practice and research

    2022  Volume 2, Issue 1, Page(s) 100046

    Abstract: Background: It is possible that preservative-free eye drops can be contaminated. The aim of this study was to assess the incidence of microbial contamination of preservative-free hospital-prepared anti-infective eye drops and investigate factors that ... ...

    Abstract Background: It is possible that preservative-free eye drops can be contaminated. The aim of this study was to assess the incidence of microbial contamination of preservative-free hospital-prepared anti-infective eye drops and investigate factors that contribute to contamination. This finding may help to raise awareness of this problem to medical healthcare staff and patients in order to prevent the transmission of microorganisms from eye drops to the patients through treatment of pre-existing eye diseases.
    Methods: Two hundred and ninety-five eye drop bottles were collected from patients attending Rajavithi Hospital Ophthalmologic outpatient and inpatient department, including both those used by patients at home and those administered in the hospital by medical staff. Samples were taken from the tips of droppers and bottles, and the residual fluid inside the bottles was then cultivated onto different culture plates. The culture results were identified and analyzed according to various factors related to both individual users and the bottles.
    Results: Seven different types of eye drops were collected and 71 (24.06%) of the 295 bottles were contaminated. Vancomycin eye drops were the most contaminated. Twenty-six different types of pathogens were identified, most frequently mold (42.98%), and the amount of contamination was higher in tips than in residual fluid inside the bottle. There was no statistically significant difference in contamination between patients used eye drops collected in outpatient units (32.14%) and medical staff used eye drops collected in inpatient settings (23.22%). The only factor that was statistically significant was the number of eye drops used per person. We found that samples from patients who used only up to 2 eye drops suffered contamination (42.8%) more than those from their counterparts who used at least 3 (22.18%),
    Conclusions: Of these preservative-free hospital preparations anti-infective eye drops, 24.06% were contaminated. The number of eye drops used per person was statistically significant in triggering contamination. There is a possibility of number of eyedrops use person may trigger contamination.
    Language English
    Publishing date 2022-03-30
    Publishing country United States
    Document type Journal Article
    ISSN 2667-3762
    ISSN (online) 2667-3762
    DOI 10.1016/j.aopr.2022.100046
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  8. Article ; Online: Economic Evaluations of Artificial Intelligence in Ophthalmology.

    Ruamviboonsuk, Paisan / Chantra, Somporn / Seresirikachorn, Kasem / Ruamviboonsuk, Varis / Sangroongruangsri, Sermsiri

    Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)

    2021  Volume 10, Issue 3, Page(s) 307–316

    Abstract: Abstract: Artificial intelligence (AI) is expected to cause significant medical quality enhancements and cost-saving improvements in ophthalmology. Although there has been a rapid growth of studies on AI in the recent years, real-world adoption of AI is ...

    Abstract Abstract: Artificial intelligence (AI) is expected to cause significant medical quality enhancements and cost-saving improvements in ophthalmology. Although there has been a rapid growth of studies on AI in the recent years, real-world adoption of AI is still rare. One reason may be because the data derived from economic evaluations of AI in health care, which policy makers used for adopting new technology, have been fragmented and scarce. Most data on economics of AI in ophthalmology are from diabetic retinopathy (DR) screening. Few studies classified costs of AI software, which has been considered as a medical device, into direct medical costs. These costs of AI are composed of initial and maintenance costs. The initial costs may include investment in research and development, and costs for validation of different datasets. Meanwhile, the maintenance costs include costs for algorithms upgrade and hardware maintenance in the long run. The cost of AI should be balanced between manufacturing price and reimbursements since it may pose significant challenges and barriers to providers. Evidence from cost-effectiveness analyses showed that AI, either standalone or used with humans, was more cost-effective than manual DR screening. Notably, economic evaluation of AI for DR screening can be used as a model for AI to other ophthalmic diseases.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Cost-Benefit Analysis ; Eye Diseases/diagnosis ; Eye Diseases/therapy ; Humans ; Ophthalmology
    Language English
    Publishing date 2021-07-13
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2756329-7
    ISSN 2162-0989 ; 2162-0989
    ISSN (online) 2162-0989
    ISSN 2162-0989
    DOI 10.1097/APO.0000000000000403
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  9. Article: Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies.

    Chaitanuwong, Pareena / Singhanetr, Panisa / Chainakul, Methaphon / Arjkongharn, Niracha / Ruamviboonsuk, Paisan / Grzybowski, Andrzej

    Neurology and therapy

    2023  Volume 12, Issue 5, Page(s) 1517–1532

    Abstract: Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of ...

    Abstract Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.
    Language English
    Publishing date 2023-07-20
    Publishing country New Zealand
    Document type Journal Article ; Review
    ISSN 2193-8253
    ISSN 2193-8253
    DOI 10.1007/s40120-023-00526-0
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  10. Article ; Online: Clinical Correlation of Retinal Fluid Fluctuation Represented by Fluctuation Index in Wet Age-Related Macular Degeneration: TOWER Study Report 2.

    Chantarasorn, Yodpong / Ruamviboonsuk, Paisan / Thoongsuwan, Somanus / Vongkulsiri, Sritatath / Kungwanpongpun, Pavinee / Hanutsaha, Prut

    Translational vision science & technology

    2023  Volume 12, Issue 10, Page(s) 2

    Abstract: Purpose: To explore outcomes and biomarkers associated with retinal fluid instability represented by a new parameter in neovascular age-related macular degeneration (nAMD).: Methods: Patients with treatment-naïve nAMD receiving anti-vascular ... ...

    Abstract Purpose: To explore outcomes and biomarkers associated with retinal fluid instability represented by a new parameter in neovascular age-related macular degeneration (nAMD).
    Methods: Patients with treatment-naïve nAMD receiving anti-vascular endothelial growth factor (VEGF) injections for a duration of 1 to 3 years were consecutively reviewed. Fluctuation Index (FI) of each eye, calculated by averaging the sum of differences in 1-mm central subfield thickness between each follow-up from months 3 to 24, was arranged into ascending order from the lowest to the highest and split equally into low, moderate, and high fluctuation groups. Outcomes were analyzed at 24 months.
    Results: Of 558 eyes, FI values showed a negative correlation with a degree-response gradient with 24-month visual improvement. After controlling for baseline best-corrected visual acuity and potential confounders, eyes with low fluctuation gained more Early Treatment Diabetic Retinopathy Study letters than those in the moderate and high fluctuation group (Δ, 10.1 and 14.0 letters, respectively). Significant best-corrected visual acuity improvement from baseline to month 24 (11.8 letters) was observed exclusively in the low fluctuation group despite the indifference in the number of injections and types of anti-VEGF drug used among groups. Patients presenting with central subfield thickness of ≥405 µm or intraretinal fluid coinciding with subretinal fluid showed a significant association with foveal thickness instability during the maintenance phase.
    Conclusions: Apart from the central subfield thickness values, unstable macular thickening represented by the FI was associated with some baseline features and may contribute to substandard visual outcomes.
    Translational relevance: FI may be a valuable tool for assessing therapeutic adequacy in the treatment of nAMD.
    MeSH term(s) Humans ; Retina/diagnostic imaging ; Wet Macular Degeneration/diagnosis ; Wet Macular Degeneration/drug therapy ; Diabetic Retinopathy
    Language English
    Publishing date 2023-10-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2674602-5
    ISSN 2164-2591 ; 2164-2591
    ISSN (online) 2164-2591
    ISSN 2164-2591
    DOI 10.1167/tvst.12.10.2
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