| A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH A TaylorWeiner, H Pokkalla, L Han, C Jia, R Huss, C Chung, H Elliott, ... Hepatology 74 (1), 133-147, 2021 | 129 | 2021 |
| A machine learning approach to liver histological evaluation predicts clinically significant portal hypertension in NASH cirrhosis J Bosch, C Chung, OM CarrascoZevallos, SA Harrison, MF Abdelmalek, ... Hepatology 74 (6), 3146-3160, 2021 | 31 | 2021 |
| Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab±ipilimumab V Baxi, G Lee, C Duan, D Pandya, DN Cohen, R Edwards, H Chang, J Li, ... Modern Pathology 35 (11), 1529-1539, 2022 | 18 | 2022 |
| Rethinking machine learning model evaluation in pathology SA Javed, D Juyal, Z Shanis, S Chakraborty, H Pokkalla, A Prakash arXiv preprint arXiv:2204.05205, 2022 | 13 | 2022 |
| Machine learning models accurately interpret liver histology in patients with nonalcoholic steatohepatitis (NASH) H Pokkalla, K Pethia, B Glass, JK Kerner, Y Gindin, L Han, R Huss, ... Hepatology 70, 121A-122A, 2019 | 11 | 2019 |
| MedAssist: automated medication kit K Gupta, A Jain, PH Vardhan, S Singh, A Amber, A Sethi 2014 Texas Instruments India Educators' Conference (TIIEC), 93-99, 2014 | 7 | 2014 |
| Machine Learning Models Identify Novel Histologic Features Predictive of Clinical Disease Progression in Patients with Advanced Fibrosis due to NASH H Pokkalla, B Glass, L Han, R Huss, K Kersey, GM Subramanian, ... 춘· 추계 학술대회 (The Liver Week) 2020 (1), 248-249, 2020 | 6 | 2020 |
| Artificial intelligence-powered retrospective analysis of PD-L1 expression in nivolumab trials of advanced non-small cell lung cancer V Baxi, A Beck, D Pandya, G Lee, C Hedvat, A Khosla, D Wang, H Elliott, ... Journal for Immunotherapy of Cancer 7, 2019 | 4 | 2019 |
| CD8+ T cells in tumor parenchyma and stroma by image analysis (IA) and gene expression profiling (GEP): Potential biomarkers for immuno-oncology (IO) therapy. PM Szabo, G Lee, S Ely, V Baxi, H Pokkalla, H Elliott, D Wang, B Glass, ... Journal of Clinical Oncology 37 (15_suppl), 2594-2594, 2019 | 4 | 2019 |
| An empirical framework for validating artificial intelligence-derived PD-L1 positivity predictions applied to urothelial carcinoma A Beck, B Glass, H Elliott, JK Kerner, A Khosla, A Lahiri J Immunother Cancer 7 (suppl 1), P730, 2019 | 4 | 2019 |
| AI-based histologic scoring enables automated and reproducible assessment of enrollment criteria and endpoints in NASH clinical trials JS Iyer, H Pokkalla, C Biddle-Snead, O Carrasco-Zevallos, M Lin, ... medRxiv, 2023 | 3 | 2023 |
| AI-based histologic measurement of NASH (AIM-NASH): a drug development tool for assessing clinical trial end points O Carrasco-Zevallos, A Taylor-Weiner, H Pokkalla, M Pouryahya, ... JOURNAL OF HEPATOLOGY 75, S254-S254, 2021 | 3 | 2021 |
| Liver biopsy graph neural networks for automated histologic scoring using the NASH CRN system J Wang, M Pouryahya, K Leidal, H Pokkalla, D Juyal, Z Shanis, A Pedawi, ... JOURNAL OF HEPATOLOGY 75, S602-S603, 2021 | 3 | 2021 |
| Abstract P5-02-02: Artificial intelligence powered predictive analysis of atypical ductal hyperplasia from digitized pathology images JK Kerner, A Cleary, S Jain, H Pokkalla, B Glass, S Grossmith, M Harary, ... Cancer Research 80 (4_Supplement), P5-02-02-P5-02-02, 2020 | 3 | 2020 |
| Integration of machine learning-based histopathology and hepatic transcriptomic data identifies genes associated with portal inflammation and ductular proliferation as … M Pouryahya, AH Taylor-Weiner, H Pokkalla, K Pethia, H Elliott, BH Glass, ... The Liver Meeting Digital Experience™, 2020 | 3 | 2020 |
| Validation of a machine learning-based approach (DELTA liver fibrosis score) for the assessment of histologic response in patients with advanced fibrosis due to NASH AH Taylor-Weiner, H Pokkalla, L Han, C Jia, R Huss, C Chung, H Elliott, ... The Liver Meeting Digital Experience™, 2020 | 2 | 2020 |
| FRI173-Machine learning models accurately interpret liver histology and are associated with disease progression in patients with primary sclerosing cholangitis N Travis, V Billaut, H Pokkalla, K Pethia, O Zevallos, B Glass, A Taylor, ... Journal of hepatology 73, S485-S486, 2020 | 2 | 2020 |
| Machine learning fibrosis models based on liver histology images accurately characterize the heterogeneity of cirrhosis due to nonalcoholic steatohepatitis (NASH) ZM Younossi, H Pokkalla, K Pethia, B Glass, JK Kerner, Y Gindin, L Han, ... Hepatology 70, 1033A-1034A, 2019 | 2 | 2019 |
| MACHINE LEARNING-BASED PREDICTION OF GEBOES SCORE AND HISTOLOGIC IMPROVEMENT AND REMISSION THRESHOLDS IN ULCERATIVE COLITIS Z Shanis, H Padigela, K Sucipto, J Shamshoian, J Li, A Walker, D Fahy, ... Gastroenterology 164 (4), S25-S26, 2023 | 1 | 2023 |
| Systems and methods for frame-based validation HV Pokkalla, HL Elliott, D Wang, BP Glass, IN Wapinski, JK Kerner, ... US Patent 11,527,319, 2022 | 1 | 2022 |
Andrew H. BeckCo-Founder and CEO at PathAI在 PathAI.com 的电子邮件经过验证
