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Ghabri H, Alqahtani MS, Ben Othman S, Al-Rasheed A, Abbas M, Almubarak HA, Sakli H, Abdelkarim MN. Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers. Sci Rep 2023; 13:17904. [PMID: 37863944 PMCID: PMC10589237 DOI: 10.1038/s41598-023-44689-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 10/11/2023] [Indexed: 10/22/2023] Open
Abstract
Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide.
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Affiliation(s)
- Haifa Ghabri
- MACS Laboratory, National Engineering School of Gabes, University of Gabes, 6029, Gabès, Tunisia
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE17RH, UK
| | - Soufiene Ben Othman
- PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia.
| | - Amal Al-Rasheed
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
| | - Hassan Ali Almubarak
- Division of Radiology, Department of Medicine, College of Medicine and Surgery, King Khalid University (KKU), Abha, Aseer, Saudi Arabia
| | - Hedi Sakli
- EITA Consulting, 5 Rue Du Chant des Oiseaux, 78360, Montesson, Montesson, France
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Neri L, Oberdier MT, van Abeelen KCJ, Menghini L, Tumarkin E, Tripathi H, Jaipalli S, Orro A, Paolocci N, Gallelli I, Dall’Olio M, Beker A, Carrick RT, Borghi C, Halperin HR. Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4805. [PMID: 37430719 PMCID: PMC10223364 DOI: 10.3390/s23104805] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.
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Affiliation(s)
- Luca Neri
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Matt T. Oberdier
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Kirsten C. J. van Abeelen
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Internal Medicine, Radboud University Medical Center, 6525 AJ Nijmegen, The Netherlands
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Ethan Tumarkin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Hemantkumar Tripathi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Sujai Jaipalli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alessandro Orro
- Institute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy
| | - Nazareno Paolocci
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Ilaria Gallelli
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Massimo Dall’Olio
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Amir Beker
- AccYouRate Group S.p.A., 67100 L’Aquila, Italy
| | - Richard T. Carrick
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Henry R. Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA
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Advanced Time-Frequency Methods for ECG Waves Recognition. Diagnostics (Basel) 2023; 13:diagnostics13020308. [PMID: 36673118 PMCID: PMC9858079 DOI: 10.3390/diagnostics13020308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 01/07/2023] [Accepted: 01/10/2023] [Indexed: 01/18/2023] Open
Abstract
ECG wave recognition is one of the new topics where only one of the ECG beat waves (P-QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart rhythm are hard to detect using either time-domain or frequency-domain features solely, and a time-frequency analysis is required to extract representative features. This paper studies the performance of two different spectrum representations, iris-spectrogram and scalogram, for different ECG beat waves in terms of recognition of normal, tachycardia, and bradycardia classes. These two different spectra are then sent to two different deep convolutional neural networks (CNN), i.e., Resnet101 and ShuffleNet, for deep feature extraction and classification. The results show that the best accuracy for detection of beats rhythm was using ResNet101 and scalogram of T-wave with an accuracy of 98.3%, while accuracy was 94.4% for detection using iris-spectrogram using also ResNet101 and QRS-Wave. Finally, based on these results we note that using deep features from time-frequency representation using one wave of ECG beat we can accurately detect basic rhythms such as normal, tachycardia, and bradycardia.
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Multi-classification neural network model for detection of abnormal heartbeat audio signals. BIOMEDICAL ENGINEERING ADVANCES 2022. [DOI: 10.1016/j.bea.2022.100048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Alqudah AM, Qazan S, Obeidat YM. Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds. Soft comput 2022; 26:13405-13429. [PMID: 36186666 PMCID: PMC9510581 DOI: 10.1007/s00500-022-07499-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/09/2022] [Indexed: 11/23/2022]
Abstract
In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. Also, we will find out the best deep learning model for this task. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. Among all proposed deep learning models, the CNN–LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The accuracy of CNN–LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification.
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Affiliation(s)
- Ali Mohammad Alqudah
- Department of Biomedical Systems and Informatics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan
| | - Shoroq Qazan
- Department of Computer Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan
| | - Yusra M Obeidat
- Department of Electronic Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan
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A lightweight hybrid deep learning system for cardiac valvular disease classification. Sci Rep 2022; 12:14297. [PMID: 35995814 PMCID: PMC9395359 DOI: 10.1038/s41598-022-18293-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 08/09/2022] [Indexed: 12/21/2022] Open
Abstract
Cardiovascular diseases (CVDs) are a prominent cause of death globally. The introduction of medical big data and Artificial Intelligence (AI) technology encouraged the effort to develop and deploy deep learning models for distinguishing heart sound abnormalities. These systems employ phonocardiogram (PCG) signals because of their lack of sophistication and cost-effectiveness. Automated and early diagnosis of cardiovascular diseases (CVDs) helps alleviate deadly complications. In this research, a cardiac diagnostic system that combined CNN and LSTM components was developed, it uses phonocardiogram (PCG) signals, and utilizes either augmented or non-augmented datasets. The proposed model discriminates five heart valvular conditions, namely normal, Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve Prolapse (MVP). The findings demonstrate that the suggested end-to-end architecture yields outstanding performance concerning all important evaluation metrics. For the five classes problem using the open heart sound dataset, accuracy was 98.5%, F1-score was 98.501%, and Area Under the Curve (AUC) was 0.9978 for the non-augmented dataset and accuracy was 99.87%, F1-score was 99.87%, and AUC was 0.9985 for the augmented dataset. Model performance was further evaluated using the PhysioNet/Computing in Cardiology 2016 challenge dataset, for the two classes problem, accuracy was 93.76%, F1-score was 85.59%, and AUC was 0.9505. The achieved results show that the proposed system outperforms all previous works that use the same audio signal databases. In the future, the findings will help build a multimodal structure that uses both PCG and ECG signals.
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