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Ohno Y, Aoki T, Endo M, Koyama H, Moriya H, Okada F, Higashino T, Sato H, Oyama-Manabe N, Haraguchi T, Arakita K, Aoyagi K, Ikeda Y, Kaminaga S, Taniguchi A, Sugihara N. Machine learning-based computer-aided simple triage (CAST) for COVID-19 pneumonia as compared with triage by board-certified chest radiologists. Jpn J Radiol 2024; 42:276-290. [PMID: 37861955 PMCID: PMC10899374 DOI: 10.1007/s11604-023-01495-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/22/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE Several reporting systems have been proposed for providing standardized language and diagnostic categories aiming for expressing the likelihood that lung abnormalities on CT images represent COVID-19. We developed a machine learning (ML)-based CT texture analysis software for simple triage based on the RSNA Expert Consensus Statement system. The purpose of this study was to conduct a multi-center and multi-reader study to determine the capability of ML-based computer-aided simple triage (CAST) software based on RSNA expert consensus statements for diagnosis of COVID-19 pneumonia. METHODS For this multi-center study, 174 cases who had undergone CT and polymerase chain reaction (PCR) tests for COVID-19 were retrospectively included. Their CT data were then assessed by CAST and consensus from three board-certified chest radiologists, after which all cases were classified as either positive or negative. Diagnostic performance was then compared by McNemar's test. To determine radiological finding evaluation capability of CAST, three other board-certified chest radiologists assessed CAST results for radiological findings into five criteria. Finally, accuracies of all radiological evaluations were compared by McNemar's test. RESULTS A comparison of diagnosis for COVID-19 pneumonia based on RT-PCR results for cases with COVID-19 pneumonia findings on CT showed no significant difference of diagnostic performance between ML-based CAST software and consensus evaluation (p > 0.05). Comparison of agreement on accuracy for all radiological finding evaluations showed that emphysema evaluation accuracy for investigator A (AC = 91.7%) was significantly lower than that for investigators B (100%, p = 0.0009) and C (100%, p = 0.0009). CONCLUSION This multi-center study shows COVID-19 pneumonia triage by CAST can be considered at least as valid as that by chest expert radiologists and may be capable for playing as useful a complementary role for management of suspected COVID-19 pneumonia patients as well as the RT-PCR test in routine clinical practice.
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Affiliation(s)
- Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan.
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
| | - Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, Kitakyusyu, Fukuoka, Japan
| | - Masahiro Endo
- Division of Diagnostic Radiology, Shizuoka Cancer Center, Sunto-Gun, Nagaizumi-Cho, Shizuoka, Japan
| | - Hisanobu Koyama
- Department of Radiology, Advanced Diagnostic Medical Imaging, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Hiroshi Moriya
- Department of Radiology, Ohara General Hospital, Fukushima, Fukushima, Japan
| | - Fumito Okada
- Department of Radiology, Oita Prefectural Hospital, Oita, Oita, Japan
| | - Takanori Higashino
- Department of Radiology, National Hospital Organization Himeji Medical Center, Himeji, Hyogo, Japan
| | - Haruka Sato
- Department of Radiology, Oita University Faculty of Medicine, Yufu, Oita, Japan
| | - Noriko Oyama-Manabe
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama, Saitama, Japan
| | - Takafumi Haraguchi
- Department of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, Kawasaki, Kanagawa, Japan
| | | | - Kota Aoyagi
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | | | | | | | - Naoki Sugihara
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
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Lee T, Wollstein G, Madu CT, Wronka A, Zheng L, Zambrano R, Schuman JS, Hu J. Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality. Transl Vis Sci Technol 2023; 12:2. [PMID: 38038606 PMCID: PMC10697175 DOI: 10.1167/tvst.12.12.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Purpose Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. Method We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. Results Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. Conclusions The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. Translational Relevance TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.
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Affiliation(s)
- TingFang Lee
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Departments of Population Health, NYU Langone Health, New York, NY, USA
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Center of Neural Science, NYU College of Arts and Sciences, New York, NY, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - Chisom T Madu
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Andrew Wronka
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Lei Zheng
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Ronald Zambrano
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Joel S Schuman
- Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA
| | - Jiyuan Hu
- Departments of Population Health, NYU Langone Health, New York, NY, USA
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Chamberlin JH, Aquino G, Schoepf UJ, Nance S, Godoy F, Carson L, Giovagnoli VM, Gill CE, McGill LJ, O'Doherty J, Emrich T, Burt JR, Baruah D, Varga-Szemes A, Kabakus IM. An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality. Acad Radiol 2022; 29:1178-1188. [PMID: 35610114 PMCID: PMC8977389 DOI: 10.1016/j.acra.2022.03.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/17/2022] [Accepted: 03/24/2022] [Indexed: 12/23/2022]
Abstract
Rationale and Objectives The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia. Materials and Methods A previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest computed tomography scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes. Results Interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). Probability of each outcome behaved as a logistic function of the opacity scoring (25% intensive care unit admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). Length of hospitalization, intensive care unit stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively). Conclusion The tested dCNN was highly predictive of inpatient outcomes, performs at a near expert level, and provides added value for clinicians in terms of prognostication and disease severity.
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