Publications by authors named "Yakub Bayhaqi"

Importance: Capturing high-quality images of the entire peripheral retina while minimizing the use of scleral depression could increase the quality of examinations for retinopathy of prematurity (ROP) while reducing neonatal stress.

Objective: To evaluate whether an investigational handheld ultra-widefield optical coherence tomography (UWF-OCT) device without scleral depression can be used to document high-quality images of the peripheral retina for use in ROP examinations.

Design, Setting, And Participants: This was a prospective, cross-sectional study in the neonatal intensive care unit at a single academic medical center.

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Objective: Isolated retinal neovascularization (IRNV) is a common finding in patients with stage 2 and 3 retinopathy of prematurity (ROP). This study aimed to further classify the clinical course and significance of these lesions (previously described as "popcorn" based on clinical appearance) in patients with ROP as visualized with ultrawidefield OCT (UWF-OCT).

Design: Single center, retrospective case series.

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Objectives: The study aimed to improve the safety and accuracy of laser osteotomy (bone surgery) by integrating optical feedback systems with an Er:YAG laser. Optical feedback consists of a real-time visual feedback system that monitors and controls the depth of laser-induced cuts and a tissue sensor differentiating tissue types based on their chemical composition. The developed multimodal feedback systems demonstrated the potential to enhance the safety and accuracy of laser surgery.

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Thermal effects during bone surgery pose a common challenge, whether using mechanical tools or lasers. An irrigation system is a standard solution to cool the tissue and reduce collateral thermal damage. In bone surgery using Er:YAG laser, insufficient irrigation raises the risk of thermal damage, while excessive water lowers ablation efficiency.

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This article presents a real-time noninvasive method for detecting bone and bone marrow in laser osteotomy. This is the first optical coherence tomography (OCT) implementation as an online feedback system for laser osteotomy. A deep-learning model has been trained to identify tissue types during laser ablation with a test accuracy of 96.

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Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and provide feedback for the ablation laser to avoid critical tissues such as bone marrow and nerve.

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This work presents a long-range and extended depth-of-focus optical coherence tomography (OCT) system using a Bessel-like beam (BLB) as a visual feedback system during laser osteotomy. We used a swept-source OCT system (  = 1310 nm) with an imaging range of 26.2 mm in the air, integrated with a high energy microsecond Er:YAG laser operating at 2.

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