RESUMEN
Overview: This study evaluates the diagnostic accuracy of a multimodal large language model (LLM), ChatGPT-4, in recognizing glaucoma using color fundus photographs (CFPs) with a benchmark dataset and without prior training or fine tuning. Methods: The publicly accessible Retinal Fundus Glaucoma Challenge "REFUGE" dataset was utilized for analyses. The input data consisted of the entire 400 image testing set. The task involved classifying fundus images into either 'Likely Glaucomatous' or 'Likely Non-Glaucomatous'. We constructed a confusion matrix to visualize the results of predictions from ChatGPT-4, focusing on accuracy of binary classifications (glaucoma vs non-glaucoma). Results: ChatGPT-4 demonstrated an accuracy of 90% with a 95% confidence interval (CI) of 87.06%-92.94%. The sensitivity was found to be 50% (95% CI: 34.51%-65.49%), while the specificity was 94.44% (95% CI: 92.08%-96.81%). The precision was recorded at 50% (95% CI: 34.51%-65.49%), and the F1 Score was 0.50. Conclusion: ChatGPT-4 achieved relatively high diagnostic accuracy without prior fine tuning on CFPs. Considering the scarcity of data in specialized medical fields, including ophthalmology, the use of advanced AI techniques, such as LLMs, might require less data for training compared to other forms of AI with potential savings in time and financial resources. It may also pave the way for the development of innovative tools to support specialized medical care, particularly those dependent on multimodal data for diagnosis and follow-up, irrespective of resource constraints.
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Objective: Breakthrough COVID-19 infections are common following immunisation with various types of vaccines. The patterns of infections have not been well established. We aimed to analyse the signs and symptoms of post vaccination infections in addition to the need for hospital admission, ER visit and supplemental oxygen in relation to age and gender. Methods: A cross-sectional cohort study was conducted in JUH from March 2021 to August 2022, we interviewed 1479 individuals who are >15 years of age and got a breakthrough infection. The statistical analysis was performed using STATA statistical software. Results: Out of the 1479 cases, 50.2 % and 69.4 % were females and less than 45 years of age respectively. Symptoms of cough, fever and headache were reported by nearly 50 % of the patients, while one-third complained of dyspnoea. We found that participants older than 45 years had worse clinical outcomes (P-value < 0.001). 13 deaths were identified in this study due to breakthrough infection, 92.3 % of them were older than 45 years (P-value < 0.001). Participants ≥45 years who experienced a breakthrough infection of COVID-19 were 0.7 times less likely to be females using adjusted logistic regression. Conclusion: This study indicates that despite more severe symptoms reported in younger patients, the major clinical outcomes were worse among older patients, which makes age a major risk for poor outcomes regardless of symptoms. Thus, older people should be evaluated carefully when presenting with mild symptoms of COVID-19 breakthrough infection. The study also confirms that there is no difference in the incidence of COVID-19 breakthrough infections between males and females. Prospective studies are needed to risk stratify COVID-19 breakthrough infections, which should take into account variants of the virus and comorbidities.
RESUMEN
Retinitis pigmentosa (RP) is often undetected in its early stages. Artificial intelligence (AI) has emerged as a promising tool in medical diagnostics. Therefore, we conducted a systematic review and meta-analysis to evaluate the diagnostic accuracy of AI in detecting RP using various ophthalmic images. We conducted a systematic search on PubMed, Scopus, and Web of Science databases on December 31, 2022. We included studies in the English language that used any ophthalmic imaging modality, such as OCT or fundus photography, used any AI technologies, had at least an expert in ophthalmology as a reference standard, and proposed an AI algorithm able to distinguish between images with and without retinitis pigmentosa features. We considered the sensitivity, specificity, and area under the curve (AUC) as the main measures of accuracy. We had a total of 14 studies in the qualitative analysis and 10 studies in the quantitative analysis. In total, the studies included in the meta-analysis dealt with 920,162 images. Overall, AI showed an excellent performance in detecting RP with pooled sensitivity and specificity of 0.985 [95%CI: 0.948-0.996], 0.993 [95%CI: 0.982-0.997] respectively. The area under the receiver operating characteristic (AUROC), using a random-effect model, was calculated to be 0.999 [95%CI: 0.998-1.000; P < 0.001]. The Zhou and Dendukuri I² test revealed a low level of heterogeneity between the studies, with [I2 = 19.94%] for sensitivity and [I2 = 21.07%] for specificity. The bivariate I² [20.33%] also suggested a low degree of heterogeneity. We found evidence supporting the accuracy of AI in the detection of RP; however, the level of heterogeneity between the studies was low.