A5047892749- Radiomics and Machine Learning in Medical Imaging
- Bladder and Urothelial Cancer Treatments
- AI in cancer detection
- Cancer Genomics and Diagnostics
- Esophageal Cancer Research and Treatment
- Pancreatic and Hepatic Oncology Research
- Ovarian cancer diagnosis and treatment
- Artificial Intelligence in Healthcare and Education
- Ophthalmology and Visual Impairment Studies
- Advanced Image and Video Retrieval Techniques
- Retinal Diseases and Treatments
- Retinal Imaging and Analysis
- Inflammatory Biomarkers in Disease Prognosis
- Glaucoma and retinal disorders
- Colorectal Cancer Screening and Detection
- Prostate Cancer Treatment and Research
- Multimodal Machine Learning Applications
- Machine Learning in Healthcare
- Generative Adversarial Networks and Image Synthesis
- Urinary and Genital Oncology Studies
- Gastric Cancer Management and Outcomes
- Advanced Vision and Imaging
- Advanced Radiotherapy Techniques
- Cancer Immunotherapy and Biomarkers
Stanford University
2020-2023
Novem (Netherlands)
2020
Eye Hospital in Bangalore
2020
204 Background: In mHSPC, no baseline biomarker reliably predicts progression-free survival (PFS). Identifying pts unlikely to benefit from current therapies could enable personalized management and better pt selection for clinical trials. Herein, we investigate the capacity of a novel AI-based digital pathology platform prognosticate in mHSPC. Methods: Pts diagnosed with mHSPC available slides undergoing systemic therapy at Huntsman Cancer Institute, University Utah were eligible. Whole...
Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers response to therapy are lacking PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach histologic feature examination that extracts a signature predictive disease-specific survival (DSS) patients with receiving adjuvant gemcitabine. demonstrate this AI-generated is associated outcomes following...
There are few markers to identify those likely recur or progress after treatment with intravesical bacillus Calmette-Guérin (BCG). We developed and validated artificial intelligence-based histologic assays that extract interpretable features from transurethral resection of bladder tumor digitized pathology images predict risk recurrence, progression, development BCG-unresponsive disease, cystectomy.
Clinical decision support tools can improve diagnostic performance or reduce variability, but they are also subject to post-deployment underperformance. Although using AI in an assistive setting offsets many concerns with autonomous medicine, systems that present all predictions equivalently fail protect against key safety concerns. We design a pipeline supports the model ecosystem of models, integrating disagreement prediction, clinical significance categorization, and prediction quality...
Purpose: Photo screeners and autorefractors have been used to screen children for amblyopia risk factors (ARF) but are limited by cost efficacy. We looked a deep learning image processing analysis-based system ARF. Methods: An android smartphone was capture images using specially coded application that modified the camera setting. algorithm developed process taken in different light conditions an automated manner predict presence of Deep models were segment face. Light settings distances...
Amblyopia is a significant public health problem. Photoscreeners have been shown to potential for screening; however, most are limited by cost and display low accuracy. The purpose of this study was validate novel artificial intelligence (AI) machine learning-based facial photoscreener "Kanna," determine its effectiveness in detecting amblyopia risk factors.A prospective that included 654 patients aged below 18 years conducted our outpatient clinic. Using an android smartphone, three images...
You have accessJournal of UrologyBladder Cancer: Non-invasive II (PD30)1 May 2024PD30-03 PREDICTING RESPONSE TO INTRAVESICAL BCG IN HIGH RISK NON-MUSCLE INVASIVE BLADDER CANCER USING AN ARTIFICIAL INTELLIGENCE-POWERED PATHOLOGY ASSAY: DEVELOPMENT AND VALIDATION INTERNATIONAL 12 CENTER COHORT Yair Lotan, Viswesh Krishna, Bryn Launer, Vrishab Siddhant Singhi, Jennifer Gordetsky, Jay B. Shah, Thomas Gerald, Eugene Shkolyar, Dickon Hayne, Andrew Redfern, Lisa Spalding, Courtney Stewart, Vikram...
You have accessJournal of UrologySurgical Technology & Simulation: Artificial Intelligence II (PD27)1 May 2024PD27-12 DEVELOPMENT AND VALIDATION OF GENERALIZABLE INTERPRETABLE AI BIOMARKERS TO PREDICT CLINICAL OUTCOMES IN BCG-TREATED PATIENTS WITH NON-MUSCLE INVASIVE BLADDER CANCER Yair Lotan, Jay B. Shah, Viswesh Krishna, Bryn Launer, Vrishab Siddhant Shingi, Jennifer Gordetsky, Thomas Gerald, Eugene Shkolyar, Dickon Hayne, Andrew Redfern, Lisa Spalding, Courtney Stewart, Vikram Narayanan,...
e16295 Background: Adjuvant chemotherapy improves survival following resection of pancreatic ductal adenocarcinoma (PDAC). A modified fluorouracil/irinotecan/oxaliplatin regimen (mFOLFIRINOX) has demonstrated improved disease free and overall survival, though gemcitabine-based monotherapy gemcitabine plus capecitabine are alternatives in less fit patients. Though there several proposed biomarkers to guide treatment decisions (GATA6, hENT1, GemPred), no biomarker is used selection clinical...
The application of deep learning to pathology assumes the existence digital whole slide images slides. However, digitization is bottlenecked by high cost precise motor stages in scanners that are needed for position information used stitching. We propose GloFlow, a two-stage method creating image using optical flow-based registration with global alignment computationally tractable graph-pruning approach. In first stage, we train an flow predictor predict pairwise translations between...
Abstract Background: The prognosis for patients diagnosed with pancreatic ductal adenocarcinoma remains poor, even after successful resection. While multiple regimens have proven to improve outcomes following resection, no biomarkers routinely used in clinical practice can predict which regimen is optimal an individual patient facilitate a precision medicine approach. Artificial intelligence (AI) approaches enable the identification of subvisual morphologic features digital scans routine...
743 Background: The prognosis of metastatic pancreatic ductal adenocarcinoma (mPDAC) remains poor with a median survival time 10-12 months. First-line treatment is largely influenced by performance status fit patients more often receiving FOLFIRINOX (FFX) than Gemcitabine+Nab-Paclitaxel (GNP). Although the two regimens have improved outcomes over gemcitabine monotherapy, no biomarkers routinely used in clinical practice can predict which regimen optimal to facilitate precision medicine...
e16566 Background: The BLASST-1 study is a multi-center phase II trial evaluating the combination of neoadjuvant nivolumab with gemcitabine-cisplatin (N+GC) for muscle-invasive bladder cancer (MIBC) patients undergoing radical cystectomy (RC). primary endpoint was pathologic down staging (PaR; ≤pT1N0). We previously reported PaR rate 65.8% (Gupta S et al. ASCO GU 2020). Given lack validated and optimal biomarkers to predict PaR, we studied association an AI-based biomarker measuring...
Many clinical AI-based decision support tools have been shown to increase diagnostic performance or reduce variability, but as with any human-AI collaboration system, these potential benefits also come risks of post-deployment underperformance. Though the use AI in an assistive setting offsets many and ethical concerns autonomous medicine, systems that present all positive negative model predictions equivalently, regardless accuracy, urgency, likelihood disagreement, fail protect against...
<h3>Objectives</h3> Histological biomarkers may produce different predictions for a single patient when using whole slide images of biopsies from sites and even serial sections the same tissue. Previous work had developed signature AI-derived morphologic features correlated with response to platinum-based chemotherapy in tubo-ovarian high-grade serous carcinoma (HGSC) specimens The Cancer Genome Atlas (TCGA) (hazard ratio: 0.35). We aim assess robustness this marker across disease sections....
<h3>Objectives</h3> Platinum-based chemotherapy is the standard of care first-line systemic treatment for patients diagnosed with advanced stages tubo-ovarian high-grade serous carcinoma (HGSC). While majority respond, roughly 15% are platinum-resistant. We aimed to develop an artificial intelligence-based platform leveraging routine pre-treatment histopathology specimens predict platinum-based response. <h3>Methods</h3> 87 from The Cancer Genome Atlas (TCGA) and 19 Stanford Hospital HGSC...