Excessive Gliosis After Vitrectomy for the Highly Myopic Macular Hole

Ye, Xin; Wang, Jun; Qiu, Wangli; Chen, Yiqi; Shen, Lijun

doi:10.1097/iae.0000000000003657

Cited by 18 publications

(9 citation statements)

References 32 publications

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“…Uncontrolled gliosis after ILM peeling is a rare occurrence [14][15][16][17]. The incidence of gliosis was reported to be higher in cases where a multi-layered ILM flap was used, high myopes, and where ILM was stuffed into the hole than just inversion of the flap [14][15][16][17].…”

Section: Discussionmentioning

confidence: 99%

Severe Gliosis Over a Closed Macular Hole Following Anterior Lens Capsular Flap Closure

Temkar,

Rajasekar,

Jayaseelan

et al. 2024

Cureus

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Various management strategies, including the use of autologous and allogenic materials, are described for the management of persistent macular holes. An anterior lens capsular flap can be used, especially when cataract surgery is also planned, for a persistent full-thickness macular hole. We report a case of a gentleman in his 60s who underwent anterior lens capsular flap closure for a persistent macular hole. There was an improvement in visual acuity. However, he developed severe gliosis over the closed hole in the postoperative period. This could be due to the proliferation of residual epithelial cells in the lens capsule, micro damage to the retina, or an exaggerated inflammatory response to a foreign tissue placed over the retinal surface.

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Section: Discussionmentioning

confidence: 99%

Severe Gliosis Over a Closed Macular Hole Following Anterior Lens Capsular Flap Closure

Temkar,

Rajasekar,

Jayaseelan

et al. 2024

Cureus

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“…This study utilized spectral domain optical coherence tomography (SD-OCT) to examine the postoperative outcomes of vitrectomy in highly myopic macular holes 66 . Sclerostin is a naturally occurring protein that inhibits the activity of cells responsible for bone formation called osteoblasts.…”

Section: Related Workmentioning

confidence: 99%

Diagnostic ability of deep learning in detection of pancreatic tumour

Dinesh¹,

Bačanin

Askar

et al. 2023

Sci Rep

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Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.

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“…Deep neural networks are employed to perform the task of matching or tracking features within soft tissues. Deep learning is a subset of artificial intelligence that excels at recognising patterns in data, making it suitable for tasks like image analysis and tracking [47][48][49] . This system utilises a specific microcontroller (STM32) for precise control of laser pulses, and it incorporates a Photomultiplier Tube (PMT) with adjustable gain to enhance the sensitivity of echo detection 50,51 .…”

Section: Related Workmentioning

confidence: 99%

Elderly and visually impaired indoor activity monitoring based on Wi-Fi and Deep Hybrid convolutional neural network

Deepa,

Bacanin,

Askar

et al. 2023

Sci Rep

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A drop in physical activity and a deterioration in the capacity to undertake daily life activities are both connected with ageing and have negative effects on physical and mental health. An Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system that keeps tabs on a person’s routine and steps in if a change in behaviour or a crisis might greatly help an elderly person or a visually impaired. These individuals may find greater freedom with the help of an EVHAM system. As the backbone of human-centric applications like actively supported living and in-home monitoring for the elderly and visually impaired, an EVHAM system is essential. Big data-driven product design is flourishing in this age of 5G and the IoT. Recent advancements in processing power and software architectures have also contributed to the emergence and development of artificial intelligence (AI). In this context, the digital twin has emerged as a state-of-the-art technology that bridges the gap between the real and virtual worlds by evaluating data from several sensors using artificial intelligence algorithms. Although promising findings have been reported by Wi-Fi-based human activity identification techniques so far, their effectiveness is vulnerable to environmental variations. Using the environment-independent fingerprints generated from the Wi-Fi channel state information (CSI), we introduce Wi-Sense. This human activity identification system employs a Deep Hybrid convolutional neural network (DHCNN). The proposed system begins by collecting the CSI with a regular Wi-Fi Network Interface Controller. Wi-Sense uses the CSI ratio technique to lessen the effect of noise and the phase offset. The t- Distributed Stochastic Neighbor Embedding (t-SNE) is used to eliminate unnecessary data further. The data dimension is decreased, and the negative effects on the environment are eliminated in this process. The resulting spectrogram of the processed data exposes the activity’s micro-Doppler fingerprints as a function of both time and location. These spectrograms are put to use in the training of a DHCNN. Based on our findings, EVHAM can accurately identify these actions 99% of the time.

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Excessive Gliosis After Vitrectomy for the Highly Myopic Macular Hole

Cited by 18 publications

References 32 publications

Severe Gliosis Over a Closed Macular Hole Following Anterior Lens Capsular Flap Closure

Severe Gliosis Over a Closed Macular Hole Following Anterior Lens Capsular Flap Closure

Diagnostic ability of deep learning in detection of pancreatic tumour

Elderly and visually impaired indoor activity monitoring based on Wi-Fi and Deep Hybrid convolutional neural network

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