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Yoon BC, Pomerantz SR, Mercaldo ND, Goyal S, L’Italien EM, Lev MH, Buch KA, Buchbinder BR, Chen JW, Conklin J, Gupta R, Hunter GJ, Kamalian SC, Kelly HR, Rapalino O, Rincon SP, Romero JM, He J, Schaefer PW, Do S, González RG. Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs. PLoS One 2023; 18:e0281900. [PMID: 36913348 PMCID: PMC10010506 DOI: 10.1371/journal.pone.0281900] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 02/03/2023] [Indexed: 03/14/2023] Open
Abstract
Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty. We incorporated awareness of uncertainty into an ML algorithm that detects intracranial hemorrhage or other urgent intracranial abnormalities and evaluated prospectively identified, 1000 consecutive noncontrast head CTs assigned to Emergency Department Neuroradiology for interpretation. The algorithm classified the scans into high (IC+) and low (IC-) probabilities for intracranial hemorrhage or other urgent abnormalities. All other cases were designated as No Prediction (NP) by the algorithm. The positive predictive value for IC+ cases (N = 103) was 0.91 (CI: 0.84-0.96), and the negative predictive value for IC- cases (N = 729) was 0.94 (0.91-0.96). Admission, neurosurgical intervention, and 30-day mortality rates for IC+ was 75% (63-84), 35% (24-47), and 10% (4-20), compared to 43% (40-47), 4% (3-6), and 3% (2-5) for IC-. There were 168 NP cases, of which 32% had intracranial hemorrhage or other urgent abnormalities, 31% had artifacts and postoperative changes, and 29% had no abnormalities. An ML algorithm incorporating uncertainty classified most head CTs into clinically relevant groups with high predictive values and may help accelerate the management of patients with intracranial hemorrhage or other urgent intracranial abnormalities.
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Affiliation(s)
- Byung C. Yoon
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Stuart R. Pomerantz
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Mass General Brigham Data Science Office, Boston, MA, United States of America
| | - Nathaniel D. Mercaldo
- Massachusetts General Hospital Institute for Technology Assessment, Boston, MA, United States of America
| | - Swati Goyal
- Mass General Brigham Data Science Office, Boston, MA, United States of America
- Department of Radiology/ Information Systems, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Eric M. L’Italien
- Mass General Brigham Data Science Office, Boston, MA, United States of America
- Department of Radiology/ Information Systems, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Michael H. Lev
- Emergency Radiology & Neuroradiology Divisions, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Karen A. Buch
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Bradley R. Buchbinder
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - John W. Chen
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Massachusetts General Hospital Center for Systems Biology (CSB), Boston, MA, United States of America
| | - John Conklin
- Emergency Radiology & Neuroradiology Divisions, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Rajiv Gupta
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Massachusetts General Hospital Consortia for Integration of Medicine and Innovative Technologies (CIMIT), Boston, MA, United States of America
- Massachusetts General Hospital CT Innovation and Advanced X-ray Imaging Science (AXIS) Center, Boston, MA, United States of America
| | - George J. Hunter
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Shahmir C. Kamalian
- Emergency Radiology & Neuroradiology Divisions, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Hillary R. Kelly
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Department of Radiology, Massachusetts Eye and Ear Institute, Harvard Medical School, Boston, MA, United States of America
| | - Otto Rapalino
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Sandra P. Rincon
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Javier M. Romero
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Julian He
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Pamela W. Schaefer
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Mass General Brigham Enterprise Radiology, Boston, MA, United States of America
| | - Synho Do
- Mass General Brigham Data Science Office, Boston, MA, United States of America
| | - Ramon Gilberto González
- Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Mass General Brigham Data Science Office, Boston, MA, United States of America
- Massachusetts General Hospital Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States of America
- * E-mail:
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