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Jeong D, Jung S, Yoon YE, Jeon J, Jang Y, Ha S, Hong Y, Cho J, Lee SA, Choi HM, Chang HJ. Artificial intelligence-enhanced automation for M-mode echocardiographic analysis: ensuring fully automated, reliable, and reproducible measurements. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:1245-1256. [PMID: 38652399 DOI: 10.1007/s10554-024-03095-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/25/2024] [Indexed: 04/25/2024]
Abstract
To enhance M-mode echocardiography's utility for measuring cardiac structures, we developed and evaluated an artificial intelligence (AI)-based automated analysis system for M-mode images through the aorta and left atrium [M-mode (Ao-LA)], and through the left ventricle [M-mode (LV)]. Our system, integrating two deep neural networks (DNN) for view classification and image segmentation, alongside an auto-measurement algorithm, was developed using 5,958 M-mode images [3,258 M-mode (LA-Ao), and 2,700 M-mode (LV)] drawn from a nationwide echocardiographic dataset collated from five tertiary hospitals. The performance of view classification and segmentation DNNs were evaluated on 594 M-mode images, while automatic measurement accuracy was tested on separate internal test set with 100 M-mode images as well as external test set with 280 images (140 sinus rhythm and 140 atrial fibrillation). Performance evaluation showed the view classification DNN's overall accuracy of 99.8% and segmentation DNN's Dice similarity coefficient of 94.3%. Within the internal test set, all automated measurements, including LA, Ao, and LV wall and cavity, resonated strongly with expert evaluations, exhibiting Pearson's correlation coefficients (PCCs) of 0.81-0.99. This performance persisted in the external test set for both sinus rhythm (PCC, 0.84-0.98) and atrial fibrillation (PCC, 0.70-0.97). Notably, automatic measurements, consistently offering multi-cardiac cycle readings, showcased a stronger correlation with the averaged multi-cycle manual measurements than with those of a single representative cycle. Our AI-based system for automatic M-mode echocardiographic analysis demonstrated excellent accuracy, reproducibility, and speed. This automated approach has the potential to improve efficiency and reduce variability in clinical practice.
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Affiliation(s)
- Dawun Jeong
- Department of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Sunghee Jung
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Ontact Health Inc, Seoul, South Korea
| | - Yeonyee E Yoon
- Ontact Health Inc, Seoul, South Korea.
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Gumi-Ro 173, Bundang-Gu, Seongnam, Gyeonggi-Do, 13620, South Korea.
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea.
| | | | | | - Seongmin Ha
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Ontact Health Inc, Seoul, South Korea
- Graduate School of Biomedical Engineering, Yonsei University College of Medicine, Seoul, South Korea
| | - Youngtaek Hong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Ontact Health Inc, Seoul, South Korea
| | | | | | - Hong-Mi Choi
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Gumi-Ro 173, Bundang-Gu, Seongnam, Gyeonggi-Do, 13620, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Ontact Health Inc, Seoul, South Korea
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
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Montgomery MK, Duan C, Manzuk L, Chang S, Cubias A, Brun S, Giddabasappa A, Jiang ZK. Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography. Transl Oncol 2024; 40:101833. [PMID: 38128467 PMCID: PMC10776660 DOI: 10.1016/j.tranon.2023.101833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 11/01/2023] [Accepted: 11/14/2023] [Indexed: 12/23/2023] Open
Abstract
Lung cancer remains a leading cause of cancer-related death, but scientists have made great strides in developing new treatments recently, partly owing to the use of genetically engineered mouse models (GEMMs). GEMM tumors represent a translational model that recapitulates human disease better than implanted models because tumors develop spontaneously in the lungs. However, detection of these tumors relies on in vivo imaging tools, specifically micro-Computed Tomography (micro-CT or µCT), and image analysis can be laborious with high inter-user variability. Here we present a deep learning model trained to perform fully automated segmentation of lung tumors without the interference of other soft tissues. Trained and tested on 100 3D µCT images (18,662 slices) that were manually segmented, the model demonstrated a high correlation to manual segmentations on the testing data (r2=0.99, DSC=0.78) and on an independent dataset (n = 12 3D scans or 2328 2D slices, r2=0.97, DSC=0.73). In a comparison against manual segmentation performed by multiple analysts, the model (r2=0.98, DSC=0.78) performed within inter-reader variability (r2=0.79, DSC=0.69) and close to intra-reader variability (r2=0.99, DSC=0.82), all while completing 5+ hours of manual segmentations in 1 minute. Finally, when applied to a real-world longitudinal study (n = 55 mice), the model successfully detected tumor progression over time and the differences in tumor burden between groups induced with different virus titers, aligning well with more traditional analysis methods. In conclusion, we have developed a deep learning model which can perform fast, accurate, and fully automated segmentation of µCT scans of murine lung tumors.
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Affiliation(s)
| | - Chong Duan
- Early Clinical Development, Pfizer Inc., 1 Portland Street, Cambridge, MA 02139, United States
| | - Lisa Manzuk
- Comparative Medicine, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States
| | - Stephanie Chang
- Comparative Medicine, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States
| | - Aiyana Cubias
- Early Clinical Development, Pfizer Inc., 1 Portland Street, Cambridge, MA 02139, United States
| | - Sonja Brun
- Oncology Research and Development, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States
| | - Anand Giddabasappa
- Comparative Medicine, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States
| | - Ziyue Karen Jiang
- Comparative Medicine, Pfizer Inc., 10646 Science Center Drive, La Jolla, CA 92121, United States.
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Lindsey ML, LeBlanc AJ, Bertrand L, Bradshaw AD, Kleinbongard P. Improving rigor and reproducibility in cardiovascular research. Am J Physiol Heart Circ Physiol 2023; 325:H866-H868. [PMID: 37656129 PMCID: PMC10659259 DOI: 10.1152/ajpheart.00524.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 08/25/2023] [Accepted: 08/25/2023] [Indexed: 09/02/2023]
Affiliation(s)
- Merry L Lindsey
- School of Graduate Studies, Meharry Medical College, Nashville, Tennessee, United States
- Research Service, Nashville Veterans Affairs Medical Center, Nashville, Tennessee, United States
| | - Amanda J LeBlanc
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, Kentucky, United States
- Cardiovascular Innovation Institute, University of Louisville, Louisville, Kentucky, United States
| | - Luc Bertrand
- Pole of Cardiovascular Research, Institute for Experimental and Clinical Research, UC Louvain, Brussels, Belgium
- WELBIO Department, WEL Research Institute, Wavre, Belgium
| | - Amy D Bradshaw
- Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States
- The Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, South Carolina, United States
| | - Petra Kleinbongard
- Institute for Pathophysiology, West German Heart and Vascular Center, University of Essen Medical School, Essen, Germany
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De Rosa L, L’Abbate S, Kusmic C, Faita F. Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals. Life (Basel) 2023; 13:1759. [PMID: 37629616 PMCID: PMC10455134 DOI: 10.3390/life13081759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/28/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND AND AIM Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models. METHODS A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines. RESULTS Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging. CONCLUSION DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.
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Affiliation(s)
- Laura De Rosa
- Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy; (L.D.R.); (F.F.)
- Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy
| | - Serena L’Abbate
- Institute of Life Sciences, Scuola Superiore Sant’Anna, 56124 Pisa, Italy;
| | - Claudia Kusmic
- Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy; (L.D.R.); (F.F.)
| | - Francesco Faita
- Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy; (L.D.R.); (F.F.)
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Alharbi AH, Towfek SK, Abdelhamid AA, Ibrahim A, Eid MM, Khafaga DS, Khodadadi N, Abualigah L, Saber M. Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm. Biomimetics (Basel) 2023; 8:313. [PMID: 37504202 PMCID: PMC10807651 DOI: 10.3390/biomimetics8030313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/03/2023] [Accepted: 07/12/2023] [Indexed: 07/29/2023] Open
Abstract
The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.
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Affiliation(s)
- Amal H Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - S K Towfek
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
| | - Abdelaziz A Abdelhamid
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Marwa M Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia
| | - Mohamed Saber
- Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt
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Bukas C, Galter I, da Silva-Buttkus P, Fuchs H, Maier H, Gailus-Durner V, Müller CL, Hrabě de Angelis M, Piraud M, Spielmann N. Echo2Pheno: a deep-learning application to uncover echocardiographic phenotypes in conscious mice. Mamm Genome 2023; 34:200-215. [PMID: 37221250 PMCID: PMC10290584 DOI: 10.1007/s00335-023-09996-x] [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: 02/17/2023] [Accepted: 04/28/2023] [Indexed: 05/25/2023]
Abstract
Echocardiography, a rapid and cost-effective imaging technique, assesses cardiac function and structure. Despite its popularity in cardiovascular medicine and clinical research, image-derived phenotypic measurements are manually performed, requiring expert knowledge and training. Notwithstanding great progress in deep-learning applications in small animal echocardiography, the focus has so far only been on images of anesthetized rodents. We present here a new algorithm specifically designed for echocardiograms acquired in conscious mice called Echo2Pheno, an automatic statistical learning workflow for analyzing and interpreting high-throughput non-anesthetized transthoracic murine echocardiographic images in the presence of genetic knockouts. Echo2Pheno comprises a neural network module for echocardiographic image analysis and phenotypic measurements, including a statistical hypothesis-testing framework for assessing phenotypic differences between populations. Using 2159 images of 16 different knockout mouse strains of the German Mouse Clinic, Echo2Pheno accurately confirms known cardiovascular genotype-phenotype relationships (e.g., Dystrophin) and discovers novel genes (e.g., CCR4-NOT transcription complex subunit 6-like, Cnot6l, and synaptotagmin-like protein 4, Sytl4), which cause altered cardiovascular phenotypes, as verified by H&E-stained histological images. Echo2Pheno provides an important step toward automatic end-to-end learning for linking echocardiographic readouts to cardiovascular phenotypes of interest in conscious mice.
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Affiliation(s)
- Christina Bukas
- Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany
| | - Isabella Galter
- Institute of Experimental Genetics, German Research Center for Environmental Health, Neuherberg, Germany
| | - Patricia da Silva-Buttkus
- Institute of Experimental Genetics, German Mouse Clinic, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Helmut Fuchs
- Institute of Experimental Genetics, German Mouse Clinic, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Holger Maier
- Institute of Experimental Genetics, German Research Center for Environmental Health, Neuherberg, Germany
| | - Valerie Gailus-Durner
- Institute of Experimental Genetics, German Mouse Clinic, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Christian L Müller
- Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Statistics, LMU München, Munich, Germany
- Center for Computational Mathematics, Flatiron Institute, New York, USA
| | - Martin Hrabě de Angelis
- Institute of Experimental Genetics, German Mouse Clinic, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
- Chair of Experimental Genetics, TUM School of Life Sciences, Technische Universität München, Freising, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
| | - Marie Piraud
- Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany
| | - Nadine Spielmann
- Institute of Experimental Genetics, German Mouse Clinic, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
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