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Kim K, Macruz F, Wu D, Bridge C, McKinney S, Al Saud AA, Sharaf E, Sesic I, Pely A, Danset P, Duffy T, Dhatt D, Buch V, Liteplo A, Li Q. Point-of-care AI-assisted stepwise ultrasound pneumothorax diagnosis. Phys Med Biol 2023; 68:205013. [PMID: 37726013 DOI: 10.1088/1361-6560/acfb70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/19/2023] [Indexed: 09/21/2023]
Abstract
Objective. Ultrasound is extensively utilized as a convenient and cost-effective method in emergency situations. Unfortunately, the limited availability of skilled clinicians in emergency hinders the wider adoption of point-of-care ultrasound. To overcome this challenge, this paper aims to aid less experienced healthcare providers in emergency lung ultrasound scans.Approach. To assist healthcare providers, it is important to have a comprehensive model that can automatically guide the entire process of lung ultrasound based on the clinician's workflow. In this paper, we propose a framework for diagnosing pneumothorax using artificial intelligence (AI) assistance. Specifically, the proposed framework for lung ultrasound scan follows the steps taken by skilled physicians. It begins with finding the appropriate transducer position on the chest to locate the pleural line accurately in B-mode. The next step involves acquiring temporal M-mode data to determine the presence of lung sliding, a crucial indicator for pneumothorax. To mimic the sequential process of clinicians, two DL models were developed. The first model focuses on quality assurance (QA) and regression of the pleural line region-of-interest, while the second model classifies lung sliding. To achieve the inference on a mobile device, a size of EfficientNet-Lite0 model was further reduced to have fewer than 3 million parameters.Main results. The results showed that both the QA and lung sliding classification models achieved over 95% in area under the receiver operating characteristic (AUC), while the ROI performance reached 89% in the dice similarity coefficient. The entire stepwise pipeline was simulated using retrospective data, yielding an AUC of 89%.Significance. The step-wise AI framework for the pneumothorax diagnosis with QA offers an intelligible guide for each clinical workflow, which achieved significantly high precision and real-time inferences.
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Affiliation(s)
- Kyungsang Kim
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States of America
| | - Fabiola Macruz
- Data Science Office, Mass General Brigham, Boston, MA, 02114, United States of America
| | - Dufan Wu
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States of America
| | - Christopher Bridge
- Data Science Office, Mass General Brigham, Boston, MA, 02114, United States of America
| | - Suzannah McKinney
- Data Science Office, Mass General Brigham, Boston, MA, 02114, United States of America
| | - Ahad Alhassan Al Saud
- Division of Ultrasound in Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States of America
| | - Elshaimaa Sharaf
- Data Science Office, Mass General Brigham, Boston, MA, 02114, United States of America
| | - Ivana Sesic
- Data Science Office, Mass General Brigham, Boston, MA, 02114, United States of America
| | - Adam Pely
- FUJIFILM Sonosite, Inc. 21919 30th Dr. SE, Bothell, WA, 98021, United States of America
| | - Paul Danset
- FUJIFILM Sonosite, Inc. 21919 30th Dr. SE, Bothell, WA, 98021, United States of America
| | - Tom Duffy
- FUJIFILM Sonosite, Inc. 21919 30th Dr. SE, Bothell, WA, 98021, United States of America
| | - Davin Dhatt
- FUJIFILM Sonosite, Inc. 21919 30th Dr. SE, Bothell, WA, 98021, United States of America
| | - Varun Buch
- FUJIFILM Sonosite, Inc. 21919 30th Dr. SE, Bothell, WA, 98021, United States of America
| | - Andrew Liteplo
- Division of Ultrasound in Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States of America
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States of America
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Bridge CP, Bizzo BC, Hillis JM, Chin JK, Comeau DS, Gauriau R, Macruz F, Pawar J, Noro FTC, Sharaf E, Straus Takahashi M, Wright B, Kalafut JF, Andriole KP, Pomerantz SR, Pedemonte S, González RG. Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging. Sci Rep 2022; 12:2154. [PMID: 35140277 PMCID: PMC8828773 DOI: 10.1038/s41598-022-06021-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 01/18/2022] [Indexed: 11/09/2022] Open
Abstract
Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learning model that used the apparent diffusion coefficient and diffusion weighted imaging series. It was trained on 6,657 MRI studies from Massachusetts General Hospital (MGH; Boston, USA). All studies were labelled positive or negative for infarct (classification annotation) with 377 having the region of interest outlined (segmentation annotation). The different annotation types facilitated training on more studies while not requiring the extensive time to manually segment every study. We initially validated the model on studies sequestered from the training set. We then tested the model on studies from three clinical scenarios: consecutive stroke team activations for 6-months at MGH, consecutive stroke team activations for 6-months at a hospital that did not provide training data (Brigham and Women’s Hospital [BWH]; Boston, USA), and an international site (Diagnósticos da América SA [DASA]; Brazil). The model results were compared to radiologist ground truth interpretations. The model performed better when trained on classification and segmentation annotations (area under the receiver operating curve [AUROC] 0.995 [95% CI 0.992–0.998] and median Dice coefficient for segmentation overlap of 0.797 [IQR 0.642–0.861]) compared to segmentation annotations alone (AUROC 0.982 [95% CI 0.972–0.990] and Dice coefficient 0.776 [IQR 0.584–0.857]). The model accurately identified infarcts for MGH stroke team activations (AUROC 0.964 [95% CI 0.943–0.982], 381 studies), BWH stroke team activations (AUROC 0.981 [95% CI 0.966–0.993], 247 studies), and at DASA (AUROC 0.998 [95% CI 0.993–1.000], 171 studies). The model accurately segmented infarcts with Pearson correlation comparing model output and ground truth volumes between 0.968 and 0.986 for the three scenarios. Acute infarct can be accurately detected and segmented on MRI in real-world clinical scenarios using a machine learning model.
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Affiliation(s)
- Christopher P Bridge
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA.,Harvard Medical School, Boston, USA.,Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Bernardo C Bizzo
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA. .,Harvard Medical School, Boston, USA. .,Department of Radiology, Massachusetts General Hospital, Boston, USA. .,Diagnósticos da América SA, São Paulo, Brazil. .,MGH & BWH Center for Clinical Data Science, Mass General Brigham, Suite 1303, Floor 13, 100 Cambridge St, Boston, MA, 02114, USA.
| | - James M Hillis
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA.,Harvard Medical School, Boston, USA.,Department of Neurology, Massachusetts General Hospital, Boston, USA
| | - John K Chin
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | - Donnella S Comeau
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | - Romane Gauriau
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | - Fabiola Macruz
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | - Jayashri Pawar
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | - Flavia T C Noro
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | - Elshaimaa Sharaf
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | | | - Bradley Wright
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | | | - Katherine P Andriole
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA.,Harvard Medical School, Boston, USA.,Department of Radiology, Brigham and Women's Hospital, Boston, USA
| | - Stuart R Pomerantz
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA.,Harvard Medical School, Boston, USA.,Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Stefano Pedemonte
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA
| | - R Gilberto González
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA.,Harvard Medical School, Boston, USA.,Department of Radiology, Massachusetts General Hospital, Boston, USA
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Ozkaya E, Fabris G, Macruz F, Suar ZM, Abderezaei J, Su B, Laksari K, Wu L, Camarillo DB, Pauly KB, Wintermark M, Kurt M. Viscoelasticity of children and adolescent brains through MR elastography. J Mech Behav Biomed Mater 2020; 115:104229. [PMID: 33387852 DOI: 10.1016/j.jmbbm.2020.104229] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 11/22/2020] [Accepted: 11/23/2020] [Indexed: 02/06/2023]
Abstract
Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G' + iG″ with the fundamental MRE parameters being the storage modulus (G'), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (μ) through viscosity (η). We observed no statistically significant variation in the parameter μ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p < 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (μ) for female brains when compared to males (p < 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.
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Affiliation(s)
- Efe Ozkaya
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | - Gloria Fabris
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | - Fabiola Macruz
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Zeynep M Suar
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | - Javid Abderezaei
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | - Bochao Su
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Kaveh Laksari
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, USA
| | - Lyndia Wu
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - David B Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Kim B Pauly
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Mehmet Kurt
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA; Biomedical Engineering and Imaging Institute, Mount Sinai Icahn School of Medicine, New York, NY, 10029, USA.
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Stewan Feltrin F, Zaninotto AL, Guirado VMP, Macruz F, Sakuno D, Dalaqua M, Magalhães LGA, Paiva WS, Andrade AFD, Otaduy MCG, Leite CC. Longitudinal changes in brain volumetry and cognitive functions after moderate and severe diffuse axonal injury. Brain Inj 2018; 32:1208-1217. [PMID: 30024781 DOI: 10.1080/02699052.2018.1494852] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
BACKGROUND AND OBJECTIVE Diffuse axonal injury (DAI) induces a long-term process of brain atrophy and cognitive deficits. The goal of this study was to determine whether there are correlations between brain volume loss, microhaemorrhage load (MHL) and neuropsychological performance during the first year after DAI. METHODS Twenty-four patients with moderate or severe DAI were evaluated at 2, 6 and 12 months post-injury. MHL was evaluated at 3 months, and brain volumetry was evaluated at 3, 6 and 12 months. The trail making test (TMT) was used to evaluate executive function (EF), and the Hopkins verbal learning test (HVLT) was used to evaluate episodic verbal memory (EVM) at 6 and 12 months. RESULTS There were significant white matter volume (WMV), subcortical grey matter volume and total brain volume (TBV) reductions during the study period (p < 0.05). MHL was correlated only with WMV reduction. EF and EVM were not correlated with MHL but were, in part, correlated with WMV and TBV reductions. CONCLUSIONS Our findings suggest that MHL may be a predictor of WMV reduction but cannot predict EF or EVM in DAI. Brain atrophy progresses over time, but patients showed better EF and EVM in some of the tests, which could be due to neuroplasticity.
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Affiliation(s)
- Fabrício Stewan Feltrin
- a Laboratory of Magnetic Resonance, LIM44, Department of Radiology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Ana Luiza Zaninotto
- b Division of Psychology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Vinícius M P Guirado
- c Division of Neurosurgery , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Fabiola Macruz
- a Laboratory of Magnetic Resonance, LIM44, Department of Radiology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Daniel Sakuno
- d Department of Radiology , Hospital Universitário HU-UEPG, Universidade Estadual de Ponta Grossa , Ponta Grossa , Brazil
| | - Mariana Dalaqua
- e Department of Radiology , Hospital Israelita Albert Einstein , São Paulo , Brazil
| | | | - Wellingson Silva Paiva
- c Division of Neurosurgery , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Almir Ferreira de Andrade
- c Division of Neurosurgery , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Maria C G Otaduy
- a Laboratory of Magnetic Resonance, LIM44, Department of Radiology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Claudia C Leite
- a Laboratory of Magnetic Resonance, LIM44, Department of Radiology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
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Chen KT, Macruz F, Gong E, Khalighi M, Zaharchuk G. P4‐310: LOW‐DOSE AMYLOID PET RECONSTRUCTION USING A PRE‐TRAINED, MULTIMODAL DEEP LEARNING NETWORK. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.07.133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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