3

M Martini, S Neifert, EK Oermann, J Gal, K Rajan, D Nistal, J Caridi. “Machine Learning with Feature Domains Elucidates Candidate Drivers of Hospital Readmission Following Spine Surgery in a Large Single-Center Patient Cohort”. Neurosurgery 2020

4

Kessler, E Oermann, R Shrivastava. Changes in Neurosurgery Education During the COVID-19 Pandemic: An Institutional Experience from a Global Epicenter. World Neurosurgery 2020

5

Neifert SN, Martini ML, Yuk FJ, McNeill IT, Caridi JM, Steinberger J, Oermann EK. Predicting Trends in Cervical Spinal Surgery in the United States from 2020 to 2040. World Neurosurgery. 2020

6

Neifert SN, Chapman EK, Hardigan T, Ladner TR, Feng R, Caridi JM, Kellner CP, Oermann EK. Increases in Subdural Hematoma with an Aging Population - the Future of American Cerebrovascular Disease. World Neurosurgery. 2020

7

Martini, ML; Neifert, SN; Yaeger, KA; Hardigan, xsTH; Ladner, TR; Nistal, DA; Lamb, CD; Kellner, CP; Macdonald, RL; Mocco, J; Oermann, EK. "Increased Risk of Transient Cerebral Ischemia Following Subarachnoid Hemorrhage in Patients with Premorbid Opioid Use Disorders: A Nationwide Analysis of Outcomes. (2020). World Neurosurgery. 2020

8

Neifert SN, Martini ML, Hanss K, Rothrock RJ, Gilligan J, Zimering J, Caridi JM, Oermann EK (2020) Large Rises in Thoracolumbar Fusions by 2040: A Cause for Concern with an Increasingly Elderly Surgical Population. World neurosurgery, https://doi.org/10.1016/j.wneu.2020.06.241

9

Neifert SN, Martini ML, Hardigan T, Ladner TR, MacDonald RL, Oermann EK (2020) Trends in Incidence and Mortality by Hospital Teaching Status and Location in Aneurysmal Subarachnoid Hemorrhage. World neurosurgery, https://doi.org/10.1016/j.wneu.2020.06.180

10

Rothrock RJ, Maragkos GA, Schupper AJ, McNeill IT, Oermann EK, Yaeger KA, Gilligan J, Bederson JB, Mocco JD (2020) By the Numbers Analysis of Effect of COVID-19 on a Neurosurgical Residency at the Epicenter. World neurosurgery, https://doi.org/10.1016/j.wneu.2020.07.063

11

Neifert SN, Chapman EK, Martini ML, Shuman WH, Schupper AJ, Oermann EK, Mocco J, Macdonald RL (2020) Aneurysmal Subarachnoid Hemorrhage: the Last Decade. Translational stroke research, https://doi.org/10.1007/s12975-020-00867-0

12

Kwon YJ, Toussie D, Azour L, Concepcion J, Eber C, Reina GA, Tang PTP, Doshi AH, Oermann EK, Costa AB (2020) Appropriate Evaluation of Diagnostic Utility of Machine Learning Algorithm Generated Images. Proceedings of the Machine Learning for Health NeurIPS Workshop, 136:179–193.

13

Martini ML, Neifert SN, Gal JS, Oermann EK, Gilligan JT, Caridi JM (2020) Drivers of Prolonged Hospitalization Following Spine Surgery: A Game-Theory-Based Approach to Explaining Machine Learning Models. The Journal of bone and joint surgery. American volume, https://doi.org/10.2106/JBJS.20.00875

14

Kwon YJ (fred), Toussie D, Finkelstein M, Cedillo MA, Maron SZ, Manna S, Voutsinas N, Eber C, Jacobi A, Bernheim A, Gupta YS, Chung MS, Fayad ZA, Glicksberg B, Oermann EK, Costa AB (2020) Combining initial radiographs and clinical variables improves deep learning prognostication of patients with COVID-19 from the emergency department. Radiology: Artificial Intelligence, e200098.

15

Martini, M. L., Oermann, E. K. Intraoperative brain tumour identification with deep learning. Nature reviews. Clinical oncology vol. 17 200–201 (2020)

16

Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM 2nd, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA (2020) Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nature Medicine, 26(1):52–58.

2019

1

ML Martini, EK Oermann, NL Opioe, F Panov, T Oxley, K Yaeger. Sensor Modalities for Brain-Computer Interface Technology: A Comprehensive Literature Review. Neurosurgery 2019

2

CJ Wang, Y Tsai, C Lee, Y Lee, AB Costa, C Hsu, EK Oermann, W Wang. Brain tumor segmentation with capsule networks versus fully convolutional neural networks. MICCAI BRATS Proceedings 2018.

3

VJ Chen, GH Meza, P Agrawa., CA Zhang, L Xie, CL Gong, CR Hoerner, S Srinivas, EK Oermann, AC Fan. Time on Therapy At Least 3 Months Correlates with Overall Survival in Metastatic Renal Cell Carcinoma. Cancers 2019.

4

AA Valliani, D Ranti, EK Oermann. Deep Learning and Neurology: A Systematic Review. Neurology and Therapy 2019.

5

Yaeger, K. A., Martini, M., Yaniv, G., Oermann, E. K. & Costa, A. B. United States regulatory approval of medical devices and software applications enhanced by artificial intelligence. Health Policy and Technology 8, 192–197 (2019).

6

EK Oermann, Y Gologorsky. Artificial Intelligence in the Clinical Neurosciences. World Neurosurgery. 2019

2018

1

Zech J, Pain M, Titano J, Badgeley M, Schefflein J, Su A, Costa A, Bederson J, Lehar J, Oermann EK (2018) Natural Language–based Machine Learning Models for the Annotation of Clinical Radiology Reports. Radiology, 287(2):570–580.

2

R Rothrock, IT McNeill, K Yaeger, EK Oermann, S Cho, J Caridi. Lumbar Lordosis Correction with Interbody Fusion: Systematic Literature Review and Analysis. World Neurosurgery 2018

3

JS Kim, V Arvind, EK Oermann, D Kaji, W Ranson, C Ugoku, AK Hussain, J Caridi, SK Cho. Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Procedures Using Machine Learning. Spine 2018

4

Zech J, Forde J, Titano J, Kaji D, Costa A, Oermann EK (2018) Detecting insertion, substitution, and deletion errors in radiology reports using neural sequence-to-sequence models. Annals of Translational Medicine, https://doi.org/10.21037/21131

5

M Badgeley, B Glicksberg, M Liu, J Titano, EK Oermann, J Dudley. CANDI, a web platform for collaboratively annotating radiographs and evaluating computer-aided diagnosis. Bioinformatics 2018

6

RS Bronheim, EK Oermann, DS Bronheim, J Caridi. Revised Cardiac Risk Index vs. ASA Status as a Predictor for Non-Cardiac Events Following Posterior Lumbar Decompression. Spine 2018

7

NC Swinburne, J Schefflein, Y Sakai, EK Oermann, JJ Titano, I Chen, S Tadayon, A Aggarwal, A Doshi, K Nael. Machine learning for semiautomated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging. Annals of Translational Medicine 2018

8

RS Bronheim, EK Oermann, DS Bronheim, JM Caridi. Revised Cardiac Risk Index as a Predictor for Myocardial Infarction and Cardiac Arrest Following Posterior Lumbar Decompression. Spine 2018

9

Titano JJ, Badgeley M, Schefflein J, Pain M, Su A, Cai M, Swinburne N, Zech J, Kim J, Bederson J, Mocco J, Drayer B, Lehar J, Cho S, Costa A, Oermann EK (2018) Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nature medicine, 24(9):1337–1341.

10

Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK (2018) Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS medicine, 15(11):e1002683. (Cover Article)

11

S Bakas, M Reyes, A Jakab, S Bauer, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv: 1811.02629

12

CJ Wang, YM Tsai, C Lee, Y Lee, AB Costa, C Hsu, EK Oermann, W Wang. Brain tumor segmentation with capsule networks versus fully convolutional networks. BraTS 2018

13

B Marinelli, M Kang, M Martini, JR Zech, J Titano, S Cho, AB Costa, EK Oermann. 3DUnet Active Transfer Learning for Automated Liver Volumetry at a High Volume Liver Transplant Center Using Surrogate Metrics Radiology Artificial Intelligence 2018

14

DA Kaji, SK Cho, NS Dangayach, AB Costa, EK Oermann. A transparent deep learning model of clinical events in the intensive care unit obtained by clinical sequencing. PLOS One 2018

Notable Publications Prior to 2017

1

Orringer DA, Pandian B, Niknafs YS, Hollon TC, Boyle J, Lewis S, Garrard M, Hervey-Jumper SL, Garton HJL, Maher CO, Heth JA, Sagher O, Wilkinson DA, Snuderl M, Venneti S, Ramkissoon SH, McFadden KA, Fisher-Hubbard A, Lieberman AP, Johnson TD, Xie XS, Trautman JK, Freudiger CW, Camelo-Piragua S (2017) Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nature biomedical engineering, 1https://doi.org/10.1038/s41551-016-0027

2

EK Oermann, A Rubinsteyn, D Ding, J Mascitelli, RM Starke, JB Bederson, H Kano, LD Lunsford, JP Sheehan, J Hammerbacher, D Kondziolka. Using a machine learning approach to predict outcomes after radiosurgery for cerebral arteriovenous malformations. Scientific Reports 2016

3

Ji M, Lewis S, Camelo-Piragua S, Ramkissoon SH, Snuderl M, Venneti S, Fisher-Hubbard A, Garrard M, Fu D, Wang AC, Heth JA, Maher CO, Sanai N, Johnson TD, Freudiger CW, Sagher O, Xie XS, Orringer DA (2015) Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy. Science translational medicine, 7(309):309ra163.

4

Oermann EK, Kress MS, Collins BT, Collins SP, Morris D, Ahalt SC, Ewend MG. Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks. Neurosurgery. 2013;72(6), 944-952

5

Oermann EK. Ode upon an Open Brain. Neurology. 2013; (80): 218-219