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Raj A, Allababidi A, Kayed H, Gerken ALH, Müller J, Schoenberg SO, Zöllner FG, Rink JS. Streamlining Acute Abdominal Aortic Dissection Management-An AI-based CT Imaging Workflow. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01164-0. [PMID: 38864947 DOI: 10.1007/s10278-024-01164-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/24/2024] [Accepted: 06/04/2024] [Indexed: 06/13/2024]
Abstract
Life-threatening acute aortic dissection (AD) demands timely diagnosis for effective intervention. To streamline intrahospital workflows, automated detection of AD in abdominal computed tomography (CT) scans seems useful to assist humans. We aimed at creating a robust convolutional neural network (CNN)-based pipeline capable of real-time screening for signs of abdominal AD in CT. In this retrospective study, abdominal CT data from AD patients presenting with AD and from non-AD patients were collected (n 195, AD cases 94, mean age 65.9 years, female ratio 35.8%). A CNN-based algorithm was developed with the goal of enabling a robust, automated, and highly sensitive detection of abdominal AD. Two sets from internal (n = 32, AD cases 16) and external sources (n = 1189, AD cases 100) were procured for validation. The abdominal region was extracted, followed by the automatic isolation of the aorta region of interest (ROI) and highlighting of the membrane via edge extraction, followed by classification of the aortic ROI as dissected/healthy. A fivefold cross-validation was employed on the internal set, and an ensemble of the 5 trained models was used to predict the internal and external validation set. Evaluation metrics included receiver operating characteristic curve (AUC) and balanced accuracy. The AUC, balanced accuracy, and sensitivity scores of the internal dataset were 0.932 (CI 0.891-0.963), 0.860, and 0.885, respectively. For the internal validation dataset, the AUC, balanced accuracy, and sensitivity scores were 0.887 (CI 0.732-0.988), 0.781, and 0.875, respectively. Furthermore, for the external validation dataset, AUC, balanced accuracy, and sensitivity scores were 0.993 (CI 0.918-0.994), 0.933, and 1.000, respectively. The proposed automated pipeline could assist humans in expediting acute aortic dissection management when integrated into clinical workflows.
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Affiliation(s)
- Anish Raj
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany.
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany.
| | - Ahmad Allababidi
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Hany Kayed
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Andreas L H Gerken
- Department of Surgery, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Julia Müller
- Mediri GmbH, Eppelheimer Straße 13, D-69115, Heidelberg, Germany
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
| | - Johann S Rink
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167, Mannheim, Germany
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Luo G, Li Z, Ge W, Ji Z, Qiao S, Pan S. Residual networks models detection of atrial septal defect from chest radiographs. LA RADIOLOGIA MEDICA 2024; 129:48-55. [PMID: 38082195 PMCID: PMC10808252 DOI: 10.1007/s11547-023-01744-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/19/2023] [Indexed: 01/25/2024]
Abstract
OBJECT The purpose of this study was to explore a machine learning-based residual networks (ResNets) model to detect atrial septal defect (ASD) on chest radiographs. METHODS This retrospective study included chest radiographs consecutively collected at our hospital from June 2017 to May 2022. Qualified chest radiographs were obtained from patients who had finished echocardiography. These chest radiographs were labeled as positive or negative for ASD based on the echocardiographic reports and were divided into training, validation, and test dataset. Six ResNets models were employed to examine and compare by using the training dataset and was tuned using the validation dataset. The area under the curve, recall, precision and F1-score were taken as the evaluation metrics for classification result in the test dataset. Visualizing regions of interest for the ResNets models using heat maps. RESULTS This study included a total of 2105 chest radiographs of children with ASD (mean age 4.14 ± 2.73 years, 54% male), patients were randomly assigned to training, validation, and test dataset with an 8:1:1 ratio. Healthy children's images were supplemented to three datasets in a 1:1 ratio with ASD patients. Following the training, ResNet-10t and ResNet-18D have a better estimation performance, with precision, recall, accuracy, F1-score, and the area under the curve being (0.92, 0.93), (0.91, 0.91), (0.90, 0.90), (0.91, 0.91) and (0.97, 0.96), respectively. Compared to ResNet-18D, ResNet-10t was more focused on the distribution of the heat map of the interest region for most chest radiographs from ASD patients. CONCLUSION The ResNets model is feasible for identifying ASD through children's chest radiographs. ResNet-10t stands out as the preferable estimation model, providing exceptional performance and clear interpretability.
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Affiliation(s)
- Gang Luo
- Heart Center, Women and Children's Hospital, Qingdao University, 6, Tongfu Road, Qingdao, 266034, China
| | - Zhixin Li
- Heart Center, Women and Children's Hospital, Qingdao University, 6, Tongfu Road, Qingdao, 266034, China
| | - Wen Ge
- Department of Radiology, Women and Children's Hospital, Qingdao University, Qingdao, 266034, China
| | - Zhixian Ji
- Heart Center, Women and Children's Hospital, Qingdao University, 6, Tongfu Road, Qingdao, 266034, China
| | - Sibo Qiao
- The School of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China
| | - Silin Pan
- Heart Center, Women and Children's Hospital, Qingdao University, 6, Tongfu Road, Qingdao, 266034, China.
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Farina JM, Pereyra M, Mahmoud AK, Scalia IG, Abbas MT, Chao CJ, Barry T, Ayoub C, Banerjee I, Arsanjani R. Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography. J Imaging 2023; 9:236. [PMID: 37998083 PMCID: PMC10672462 DOI: 10.3390/jimaging9110236] [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: 08/30/2023] [Revised: 10/18/2023] [Accepted: 10/24/2023] [Indexed: 11/25/2023] Open
Abstract
Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.
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Affiliation(s)
- Juan M. Farina
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA (M.P.); (M.T.A.); (T.B.)
| | - Milagros Pereyra
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA (M.P.); (M.T.A.); (T.B.)
| | - Ahmed K. Mahmoud
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA (M.P.); (M.T.A.); (T.B.)
| | - Isabel G. Scalia
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA (M.P.); (M.T.A.); (T.B.)
| | - Mohammed Tiseer Abbas
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA (M.P.); (M.T.A.); (T.B.)
| | - Chieh-Ju Chao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Timothy Barry
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA (M.P.); (M.T.A.); (T.B.)
| | - Chadi Ayoub
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA (M.P.); (M.T.A.); (T.B.)
| | - Imon Banerjee
- Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA;
| | - Reza Arsanjani
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA (M.P.); (M.T.A.); (T.B.)
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Hassan S, Dhimish M. Dual spin max pooling convolutional neural network for solar cell crack detection. Sci Rep 2023; 13:11099. [PMID: 37423922 DOI: 10.1038/s41598-023-38177-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023] Open
Abstract
This paper presents a solar cell crack detection system for use in photovoltaic (PV) assembly units. The system utilizes four different Convolutional Neural Network (CNN) architectures with varying validation accuracy to detect cracks, microcracks, Potential Induced Degradations (PIDs), and shaded areas. The system examines the electroluminescence (EL) image of a solar cell and determines its acceptance or rejection status based on the presence and size of the crack. The proposed system was tested on various solar cells and achieved a high degree of accuracy, with an acceptance rate of up to 99.5%. The system was validated with thermal testing using real-world cases, such as shaded areas and microcracks, which were accurately predicted by the system. The results show that the proposed system is a valuable tool for evaluating the condition of PV cells and can lead to improved efficiency. The study also shows that the proposed CNN model outperforms previous studies and can have significant implications for the PV industry by reducing the number of defective cells and improving the overall efficiency of PV assembly units.
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Affiliation(s)
- Sharmarke Hassan
- Photovoltaics Laboratory, School of Physics, Engineering and Technology, University of York, York, YO10 5DD, UK.
| | - Mahmoud Dhimish
- Photovoltaics Laboratory, School of Physics, Engineering and Technology, University of York, York, YO10 5DD, UK
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