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We present a unique case of a 36-year-old female presenting with features suggestive of bilateral combined vascular occlusion, hearing loss, and encephalopathy. Multimodal imaging was done for both eyes fundus evaluation including wide-field color fundus photography, optical coherence tomography, and fundus fluorescein angiography. After extensive ocular and systemic investigations, she was diagnosed to have Susac syndrome (SS). She was referred to a neurologist and otologist for systemic evaluation and underwent laser photocoagulation in both eyes, followed by pars plana vitrectomy in her left eye. Combined bilateral retinal vascular occlusion in association with SS is very rare.
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BACKGROUND: The integration of artificial intelligence (AI) into the healthcare domain is a monumental shift with profound implications for diagnostics, medical interventions, and the overall structure of healthcare systems. PURPOSE: This study explores the transformative journey of foundation AI models in healthcare, shedding light on the challenges, ethical considerations, and vast potential they hold for improving patient outcome and system efficiency. Notably, in this investigation we observe a relatively slow adoption of AI within the public sector of healthcare. The evolution of AI in healthcare is un-paralleled, especially its prowess in revolutionizing diagnostic processes. RESULTS: This research showcases how these foundational models can unravel hidden patterns within complex medical datasets. The impact of AI reverberates through medical interventions, encompassing pathology, imaging, genomics, and personalized healthcare, positioning AI as a cornerstone in the quest for precision medicine. The paper delves into the applications of generative AI models in critical facets of healthcare, including decision support, medical imaging, and the prediction of protein structures. The study meticulously evaluates various AI models, such as transfer learning, RNN, autoencoders, and their roles in the healthcare landscape. A pioneering concept introduced in this exploration is that of General Medical AI (GMAI), advocating for the development of reusable and flexible AI models. CONCLUSION: The review article discusses how AI can revolutionize healthcare by stressing the significance of transparency, fairness and accountability, in AI applications regarding patient data privacy and biases. By tackling these issues and suggesting a governance structure the article adds to the conversation about AI integration in healthcare environments.
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PURPOSE: To compare AngioTool (AT) vascular parameters (VP) between MacTel2 eyes and normal eyes. Secondary outcome measures were to correlate VP with BCVA and to analyze VP between various grades of Simple MacTel Classification. METHODS: This is a retrospective study. SD OCTA images of the superficial vascular complex (SVC) and deep capillary complex (DVC) were exported into Image J and AT. The explant area (EA), vessel area (VA), vessel percentage area (VPA), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL), total number of endpoints (TNE) and mean E lacunarity (MEL) were studied. RESULTS: Group 1 had 120 MacTel2 eyes. Group 2 had 60 age-matched normal eyes. All VP were significantly different between the two groups except EA and TNE in both complexes. None of the VP had a correlation with BCVA. Interquadrant analysis (IQA) in SVC and DVC showed statistical significance in VPA, AVL and JD and in AVL, TNE, JD, VPA respectively. Post hoc analysis in SVC and DVC showed statistical significance in TNJ, JD, TVL and AVL between grade 1 and grade 3, and in VA, VPA, TNJ, JD, TVL and MEL between grade 0 and grade 3 respectively. CONCLUSION: VP were affected in MacTel2 eyes. VP did not correlate with BCVA. Occurrence of pigmentation is an important event in the progression of disease. AT may provide quantitative markers to measure disease progression.
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PURPOSE: To compare the visual outcomes and complication rates between the extra-ocular needle-guided haptic insertion technique (XNIT) and the conventional handshake (HS) technique of scleral fixation intra-ocular lens (SFIOL). METHODS: In this retrospective study, we retrieved data of those patients who had undergone SFIOL surgery from January 2018 to May 2022 at our institute for aphakia following either a complicated cataract surgery or an ocular trauma and had a minimum follow-up of 3 months. RESULTS: Of the 156 eyes, the HS technique was done in 80 eyes and the remaining 76 eyes with XNIT. At 3 months follow-up visit, there was no significant difference in the median best corrected visual acuity (BCVA) ( P = 0.988) and uncorrected visual acuity (UCVA) ( P = 0.765) between the two techniques. There was no statistically significant difference between pre-operative median BCVA and post-operative UCVA in XNIT ( P = 0.961) and the HS technique ( P = 0.831) at 3 months follow-up visit. The complication rates between the two techniques were minimal and comparable. The most common post-operative complication was corneal edema. The incidence of cystoid macular edema was slightly more in the XNIT group but not statistically significant ( P = 0.05). Two patients in the HS group developed retinal detachment, which settled after repeat surgery. CONCLUSION: The newer XNIT technique was found to be as safe and effective as compared to the conventional HS technique.