Publications by authors named "Timothy C Chang"

Background: Detection of bladder tumors under white light cystoscopy (WLC) is challenging yet impactful on treatment outcomes. Artificial intelligence (AI) holds the potential to improve tumor detection; however, its application in the real-time setting remains unexplored. AI has been applied to previously recorded images for post hoc analysis.

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Only 60-75% of conventional kidney stone surgeries achieve complete stone-free status. Up to 30% of patients with residual fragments <2 mm in size experience subsequent stone-related complications. Here we demonstrate a stone retrieval technology in which fragments are rendered magnetizable with a magnetic hydrogel so that they can be easily retrieved with a simple magnetic tool.

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Background: Kidney stone disease is common and can lead to complications such as AKI, urinary tract obstruction, and urosepsis. In kidney transplant recipients, complications from kidney stone events can also lead to rejection and allograft failure. There is limited information on the incidence of kidney stone events in transplant recipients.

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With the advent of electronic medical records and digitalization of health care over the past 2 decades, artificial intelligence (AI) has emerged as an enabling tool to manage complex datasets and deliver streamlined data-driven patient care. AI algorithms have the ability to extract meaningful signal from complex datasets through an iterative process akin to human learning. Through advancements over the past decade in deep learning, AI-driven innovations have accelerated applications in health care.

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Introduction: Surgical management of penile cancer depends on accurate margin assessment and staging. Advanced optical imaging technologies may improve penile biopsy and organ-sparing treatment. We evaluated the feasibility of confocal laser endomicroscopy for intraoperative assessment of benign and malignant penile tissue.

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Article Synopsis
  • Adequate bladder tumor detection is crucial during transurethral resection (TURBT) to minimize cancer recurrence, but standard white light cystoscopy misses about 20% of tumors.
  • Researchers developed a deep learning algorithm called CystoNet to enhance tumor detection during cystoscopy by using video frames of confirmed bladder cancers for training and testing the model.
  • CystoNet demonstrated impressive results in a validation study, showing 90.9% sensitivity and 98.6% specificity, indicating it could significantly improve bladder cancer detection and surgical outcomes.
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Investigating enzyme activity is central to our understanding of biological function, and the design of biocatalysts continues to find applications in synthesis. While a role for active site residues can be proposed based on structure and mechanism, our understanding of the catalytic importance for residues surrounding the active site is less well understood. In triosephosphate isomerase (TIM), Glu97 is situated adjacent to the active site and is found in essentially all sequences.

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Transurethral resection of bladder tumor (TURBT) under white light cystoscopy (WLC) is the cornerstone for the diagnosis, removal and local staging of non-muscle invasive bladder cancer (NMIBC). Despite technological improvements over the decades, significant shortcomings remain with WLC for tumor detection, thereby impacting the surgical quality and contributing to tumor recurrence and progression. Enhanced cystoscopy modalities such as blue light cystoscopy (BLC) and narrow band imaging (NBI) aid resections by highlighting tumors that might be missed on WLC.

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A combination of optical imaging technologies with cancer-specific molecular imaging agents is a potentially powerful strategy to improve cancer detection and enable image-guided surgery. Bladder cancer is primarily managed endoscopically by white light cystoscopy with suboptimal diagnostic accuracy. Emerging optical imaging technologies hold great potential for improved diagnostic accuracy but lack imaging agents for molecular specificity.

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Probe-based confocal laser endomicroscopy (CLE) is an emerging optical imaging technology that enables real-time in vivo microscopy of mucosal surfaces during standard endoscopy. With applications currently in the respiratory and gastrointestinal tracts, CLE has also been explored in the urinary tract for bladder cancer diagnosis. Cellular morphology and tissue microarchitecture can be resolved with micron scale resolution in real time, in addition to dynamic imaging of the normal and pathological vasculature.

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Background And Purpose: Emerging optical imaging technologies such as confocal laser endomicroscopy (CLE) hold promise in improving bladder cancer diagnosis. The purpose of this study was to determine the interobserver agreement of image interpretation using CLE for bladder cancer.

Methods: Experienced CLE urologists (n=2), novice CLE urologists (n=6), pathologists (n=4), and nonclinical researchers (n=5) were recruited to participate in a 2-hour computer-based training consisting of a teaching and validation set of intraoperative white light cystoscopy (WLC) and CLE video sequences from patients undergoing transurethral resection of bladder tumor.

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