Causal factors for late presentation of retinal detachment

Anguita, Rodrigo; Ting, Magdalene Yin Lin; Makuloluwa, Achini; Charteris, David G.

doi:10.1038/s41433-022-02109-z

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…In this research the anatomical success was 76%, which is in the range reported in literature (53-81%). 1 In a study by Anguita R. et al, mean duration of visual loss was 12.7±21.3 weeks and the overall primary success rate was 69%. The baseline BCVA was 20/500, and at the last follow-up was 20/160 which was significantly improved.…”

Section: Discussionmentioning

confidence: 95%

“…Retinal detachment (RD) is a significant contributor to vision loss and with early intervention more than 80% of retinal detachment cases can be successfully treated with positive visual outcomes. 1 Contrarily, late presentation of retinal detachment has often been reported in low-income countries and associated with non-affordability, lack of access to healthcare, and awareness. 1 The incidence of RD is 13:100,000 person/ year and males are more likely to develop the condition than females.…”

Section: Introductionmentioning

confidence: 99%

“…1 Contrarily, late presentation of retinal detachment has often been reported in low-income countries and associated with non-affordability, lack of access to healthcare, and awareness. 1 The incidence of RD is 13:100,000 person/ year and males are more likely to develop the condition than females. [2][3][4] Rhegmatogenous RD is a surgical emergency of the eye and a common, vision threatening ophthalmic condition.…”

Section: Introductionmentioning

confidence: 99%

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Visual and Surgical Outcomes in Late Presentation of Retinal Detachment

Talpur,

Fariha Sher Wali,

Memon

et al. 2024

pak J Ophthalmol

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Purpose: To evaluate the frequency of treatment success in terms of visual and surgical outcomes in patients with late presentation of retinal detachment. Study Design: Quasi experimental. Place and Duration of Study: Sindh institute of Ophthalmology and visual Sciences from June 2022 to January 2023. Methods: A total of 50 eyes of 50 patients with delayed presentation of retinal detachment who underwent Pars Plana Vitrectomy (PPV) were included and followed up for 6 months. Preand post-surgery Best Corrected Visual Acuity (BCVA) and anatomical attachment of retina were comprehensively analyzed. Factors affecting an atomical success were also studied. Results: Mean age of patients was 47±18 years. Twenty seven patients were (54%) were more than 45 years of age and 32(64%) were males. Thirty eight (76%) patients achieved successful anatomical attachment of retina at 6th month follow-up. Post-operative BCVA also showed improvement in vision.Risk factors identified for poor anatomical and visual outcome following PPV for retinal detachment are; PVR advance stage, longer duration of visual symptoms and poor-presenting BCVA. Age, sex, laterality, Pre-operative BCVA, lens status, diagnosis, and type of surgery showed insignificant associations (P >0.05). Conclusion: Anatomical failure and poor functional outcomes appear to be mostly caused by PVR. PVR, longer duration of visual symptoms and poor-presenting VA are risk factors for poor anatomical and visual outcome following PPV surgery for RD. Late presentations to clinics can decrease with mass awareness. The frequency of proliferative vitreoretinopathy would decline, and visual results would be improved.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visual and Surgical Outcomes in Late Presentation of Retinal Detachment

Talpur,

Fariha Sher Wali,

Memon

et al. 2024

pak J Ophthalmol

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“…In general, the quality of clinical information provided by the interactive LLMs is variable (77.4%, 15.4%–100%) (see Table 4). 21,59–84 The accuracy of the later version of ChatGPT (GPT‐4) was higher (84.6%, 50%–92%) than the earlier versions (GPT‐3: 71.5%, 40%–80% and GPT‐3.5: 73%, 15.4%–88%), Bing Chat (78.5%, 46.7%–100%) and Google Bard (54.1%, 40.5%–72%). Compared to GPT‐3.5 and Google Bard, GPT‐4 exhibited a reduced proportion of responses that received poor ratings.…”

Section: Use Of Llms In Eyecarementioning

confidence: 99%

Utility of artificial intelligence‐based large language models in ophthalmic care

Biswas,

Davies,

Sheppard

et al. 2024

Ophthalmic Physiologic Optic

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PurposeWith the introduction of ChatGPT, artificial intelligence (AI)‐based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human‐like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under‐reported.Recent FindingsHitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question‐based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT‐4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI‐based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible‐sounding ‘fake’ responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI‐based LLMs.SummaryOphthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision‐making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world.

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