| A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain Y Ding, JH Sohn, MG Kawczynski, H Trivedi, R Harnish, NW Jenkins, ... Radiology 290 (2), 456-464, 2019 | 638 | 2019 |
| Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ... JAMA network open 3 (3), e200265-e200265, 2020 | 367 | 2020 |
| Deep learning to predict geographic atrophy area and growth rate from multimodal imaging N Anegondi, SS Gao, V Steffen, RF Spaide, SVR Sadda, FG Holz, C Rabe, ... Ophthalmology Retina 7 (3), 243-252, 2023 | 31 | 2023 |
| Development of deep learning models to predict best-corrected visual acuity from optical coherence tomography MG Kawczynski, T Bengtsson, J Dai, JJ Hopkins, SS Gao, JR Willis Translational vision science & technology 9 (2), 51-51, 2020 | 31 | 2020 |
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| Automatic labeling of special diagnostic mammography views from images and DICOM headers DS Lituiev, H Trivedi, M Panahiazar, B Norgeot, Y Seo, B Franc, ... Journal of digital imaging 32, 228-233, 2019 | 14 | 2019 |
| Geographic atrophy segmentation using multimodal deep learning T Spaide, J Jiang, J Patil, N Anegondi, V Steffen, MG Kawczynski, ... Translational Vision Science & Technology 12 (7), 10-10, 2023 | 8 | 2023 |
| Predicting geographic atrophy growth rate from fundus autofluorescence images using deep neural networks N Anegondi, Q Yang, M Kawczynski, V Steffen, SS Gao Multimodal Biomedical Imaging XVI 11634, 116340E, 2021 | 6 | 2021 |
| Machine learning to predict response to ranibizumab in neovascular age-related macular degeneration A Maunz, L Barras, MG Kawczynski, J Dai, AY Lee, RF Spaide, J Sahni, ... Ophthalmology Science 3 (4), 100319, 2023 | 3 | 2023 |
| Using Deep Learning to Process Images of the Eye to Predict Visual Acuity MG Kawczynski, T Bengtsson, J Dai, SS Gao, JR Willis US Patent App. 17/590,816, 2022 | 2 | 2022 |
| Analysis of numerical feature extraction from automated geographic atrophy segmentation T Spaide, J Patil, J Jiang, N Anegondi, M Kawczynski, V Steffen, SS Gao Investigative Ophthalmology & Visual Science 62 (8), 2124-2124, 2021 | 2 | 2021 |
| Machine Learning to Predict Faricimab Treatment Outcome in Neovascular Age-Related Macular Degeneration Y Kikuchi, MG Kawczynski, N Anegondi, A Neubert, J Dai, D Ferrara, ... Ophthalmology Science 4 (2), 100385, 2024 | 1 | 2024 |
| Geographic atrophy lesion segmentation using a deep learning network (U-net) J Patil, M Kawczynski, SS Gao, AF Coimbra Investigative Ophthalmology & Visual Science 60 (9), 1459-1459, 2019 | 1 | 2019 |
| Treatment outcome prediction for neovascular age-related macular degeneration using baseline characteristics NS Anegondi, J Dai, MG Kawczynski, YA Kikuchi US Patent App. 18/482,237, 2024 | | 2024 |
| Multimodal geographic atrophy lesion segmentation NS Anegondi, SS Gao, J Jiang, MG Kawczynski, J Patil, TC Spaide US Patent App. 18/304,006, 2023 | | 2023 |
| Prediction of geographic-atrophy progression using segmentation and feature evaluation J Patil, NS Anegondi, AJF Coimbra, SS Gao, MG Kawczynski US Patent App. 17/914,737, 2023 | | 2023 |
| Deep neural network framework for processing oct images to predict treatment intensity MG Kawczynski, JR Willis, NGT Bengtsson, J Dai, SS Gao US Patent App. 17/782,497, 2023 | | 2023 |
| Identification of choroidal neovascularization activity using deep learning X Wang, SS Gao, M Kawczynski, N Anegondi, A Thalhammer, ... Investigative Ophthalmology & Visual Science 62 (8), 2141-2141, 2021 | | 2021 |
| Prediction of best-corrected visual acuity (BCVA) from color fundus photography (CFP) using deep learning (DL) M Kawczynski, N Anegondi, Q Yang, JR Willis, T Bengtsson, SS Gao, ... Investigative Ophthalmology & Visual Science 62 (8), 126-126, 2021 | | 2021 |
| A pilot study using machine learning (ML) to predict treatment outcomes in patients with neovascular age-related macular degeneration (nAMD) using phase 2 trial data Y Kikuchi, M Kawczynski, N Anegondi, J Dai, CQ Ruiz Investigative Ophthalmology & Visual Science 62 (8), 82-82, 2021 | | 2021 |
