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Xu YD, Tang Y, Zhang Q, Zhao ZY, Zhao CK, Fan PL, Jin YJ, Ji ZB, Han H, Xu HX, Shi YL, Xu BH, Li XL. Automatic detection of thyroid nodules with a real-time artificial intelligence system in a real clinical scenario and the associated influencing factors. Clin Hemorheol Microcirc 2024:CH242099. [PMID: 38489169 DOI: 10.3233/ch-242099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
BACKGROUND At present, most articles mainly focused on the diagnosis of thyroid nodules by using artificial intelligence (AI), and there was little research on the detection performance of AI in thyroid nodules. OBJECTIVE To explore the value of a real-time AI based on computer-aided diagnosis system in the detection of thyroid nodules and to analyze the factors influencing the detection accuracy. METHODS From June 1, 2022 to December 31, 2023, 224 consecutive patients with 587 thyroid nodules were prospective collected. Based on the detection results determined by two experienced radiologists (both with more than 15 years experience in thyroid diagnosis), the detection ability of thyroid nodules of radiologists with different experience levels (junior radiologist with 1 year experience and senior radiologist with 5 years experience in thyroid diagnosis) and real-time AI were compared. According to the logistic regression analysis, the factors influencing the real-time AI detection of thyroid nodules were analyzed. RESULTS The detection rate of thyroid nodules by real-time AI was significantly higher than that of junior radiologist (P = 0.013), but lower than that of senior radiologist (P = 0.001). Multivariate logistic regression analysis showed that nodules size, superior pole, outside (near carotid artery), close to vessel, echogenicity (isoechoic, hyperechoic, mixed-echoic), morphology (not very regular, irregular), margin (unclear), ACR TI-RADS category 4 and 5 were significant independent influencing factors (all P < 0.05). With the combination of real-time AI and radiologists, junior and senior radiologist increased the detection rate to 97.4% (P < 0.001) and 99.1% (P = 0.015) respectively. CONCLUSONS The real-time AI has good performance in thyroid nodule detection and can be a good auxiliary tool in the clinical work of radiologists.
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Affiliation(s)
- Ya-Dan Xu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
- Shanghai Institute of Medical Imaging, Xuhui District, Shanghai, China
| | - Yang Tang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
| | - Qi Zhang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
| | - Zheng-Yong Zhao
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
| | - Chong-Ke Zhao
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
| | - Pei-Li Fan
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
| | - Yun-Jie Jin
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
| | - Zheng-Biao Ji
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
| | - Hong Han
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
- Shanghai Institute of Medical Imaging, Xuhui District, Shanghai, China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
- Shanghai Institute of Medical Imaging, Xuhui District, Shanghai, China
| | - Yi-Lei Shi
- Med Imaging AI Co., Ltd, Binhu District, Wuxi, China
| | - Ben-Hua Xu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
| | - Xiao-Long Li
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai, China
- Institute of Ultrasound in Medicine and Engineering, Fudan University, Xuhui District, Shanghai, China
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Policastro P, Mesin L. Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications. Bioengineering (Basel) 2023; 10:1076. [PMID: 37760178 PMCID: PMC10525913 DOI: 10.3390/bioengineering10091076] [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: 07/29/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
The inferior vena cava (IVC) is the largest vein in the body. It returns deoxygenated blood to the heart from the tissues placed under the diaphragm. The size and dynamics of the IVC depend on the blood volume and right atrial pressure, which are important indicators of a patient's hydration and reflect possible pathological conditions. Ultrasound (US) assessment of the IVC is a promising technique for evaluating these conditions, because it is fast, non-invasive, inexpensive, and without side effects. However, the standard M-mode approach for measuring IVC diameter is prone to errors due to the vein movements during respiration. B-mode US produces two-dimensional images that better capture the IVC shape and size. In this review, we discuss the pros and cons of current IVC segmentation techniques for B-mode longitudinal and transverse views. We also explored several scenarios where automated IVC segmentation could improve medical diagnosis and prognosis.
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Affiliation(s)
| | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy;
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3
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Yadav N, Dass R, Virmani J. Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images. Med Biol Eng Comput 2023:10.1007/s11517-023-02849-4. [PMID: 37353695 DOI: 10.1007/s11517-023-02849-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 05/17/2023] [Indexed: 06/25/2023]
Abstract
Encoder-decoder-based semantic segmentation models classify image pixels into the corresponding class, such as the ROI (region of interest) or background. In the present study, simple / dilated convolution / series / directed acyclic graph (DAG)-based encoder-decoder semantic segmentation models have been implemented, i.e., SegNet (VGG16), SegNet (VGG19), U-Net, mobileNetv2, ResNet18, ResNet50, Xception and Inception networks for the segment TTUS(Thyroid Tumor Ultrasound) images. Transfer learning has been used to train these segmentation networks using original and despeckled TTUS images. The performance of the networks has been calculated using mIoU and mDC metrics. Based on the exhaustive experiments, it has been observed that ResNet50-based segmentation model obtained the best results objectively with values 0.87 for mIoU, 0.94 for mDC, and also according to radiologist opinion on shape, margin, and echogenicity characteristics of segmented lesions. It is noted that the segmentation model, namely ResNet50, provides better segmentation based on objective and subjective assessment. It may be used in the healthcare system to identify thyroid nodules accurately in real time.
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Affiliation(s)
- Niranjan Yadav
- Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India.
| | - Rajeshwar Dass
- Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India
| | - Jitendra Virmani
- Central Scientific Instruments Organization, Council of Scientific and Industrial Research, Chandigarh, 160030, India
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4
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Hasan Z, Key S, Habib AR, Wong E, Aweidah L, Kumar A, Sacks R, Singh N. Convolutional Neural Networks in ENT Radiology: Systematic Review of the Literature. Ann Otol Rhinol Laryngol 2023; 132:417-430. [PMID: 35651308 DOI: 10.1177/00034894221095899] [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] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Convolutional neural networks (CNNs) represent a state-of-the-art methodological technique in AI and deep learning, and were specifically created for image classification and computer vision tasks. CNNs have been applied in radiology in a number of different disciplines, mostly outside otolaryngology, potentially due to a lack of familiarity with this technology within the otolaryngology community. CNNs have the potential to revolutionize clinical practice by reducing the time required to perform manual tasks. This literature search aims to present a comprehensive systematic review of the published literature with regard to CNNs and their utility to date in ENT radiology. METHODS Data were extracted from a variety of databases including PubMED, Proquest, MEDLINE Open Knowledge Maps, and Gale OneFile Computer Science. Medical subject headings (MeSH) terms and keywords were used to extract related literature from each databases inception to October 2020. Inclusion criteria were studies where CNNs were used as the main intervention and CNNs focusing on radiology relevant to ENT. Titles and abstracts were reviewed followed by the contents. Once the final list of articles was obtained, their reference lists were also searched to identify further articles. RESULTS Thirty articles were identified for inclusion in this study. Studies utilizing CNNs in most ENT subspecialties were identified. Studies utilized CNNs for a number of tasks including identification of structures, presence of pathology, and segmentation of tumors for radiotherapy planning. All studies reported a high degree of accuracy of CNNs in performing the chosen task. CONCLUSION This study provides a better understanding of CNN methodology used in ENT radiology demonstrating a myriad of potential uses for this exciting technology including nodule and tumor identification, identification of anatomical variation, and segmentation of tumors. It is anticipated that this field will continue to evolve and these technologies and methodologies will become more entrenched in our everyday practice.
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Affiliation(s)
- Zubair Hasan
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
| | - Seraphina Key
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC, Australia
| | - Al-Rahim Habib
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Eugene Wong
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
| | - Layal Aweidah
- Faculty of Medicine, University of Notre Dame, Darlinghurst, NSW, Australia
| | - Ashnil Kumar
- School of Biomedical Engineering, Faculty of Engineering, University of Sydney, Darlington, NSW, Australia
| | - Raymond Sacks
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Concord Hospital, Concord, NSW, Australia
| | - Narinder Singh
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
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Kunapinun A, Dailey MN, Songsaeng D, Parnichkun M, Keatmanee C, Ekpanyapong M. Improving GAN Learning Dynamics for Thyroid Nodule Segmentation. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:416-430. [PMID: 36424307 DOI: 10.1016/j.ultrasmedbio.2022.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/14/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Thyroid nodules are lesions requiring diagnosis and follow-up. Tools for detecting and segmenting nodules can help physicians with this diagnosis. Besides immediate diagnosis, automated tools can also enable tracking of the probability of malignancy over time. This paper demonstrates a new algorithm for segmenting thyroid nodules in ultrasound images. The algorithm combines traditional supervised semantic segmentation with unsupervised learning using GANs. The hybrid approach has the potential to upgrade the semantic segmentation model's performance, but GANs have the well-known problems of unstable learning and mode collapse. To stabilize the training of the GAN model, we introduce the concept of closed-loop control of the gain on the loss output of the discriminator. We find gain control leads to smoother generator training and avoids the mode collapse that typically occurs when the discriminator learns too quickly relative to the generator. We also find that the combination of the supervised and unsupervised learning styles encourages both low-level accuracy and high-level consistency. As a test of the concept of controlled hybrid supervised and unsupervised semantic segmentation, we introduce a new model named the StableSeg GAN. The model uses DeeplabV3+ as the generator, Resnet18 as the discriminator, and uses PID control to stabilize the GAN learning process. The performance of the new model in terms of IoU is better than DeeplabV3+, with mean IoU of 81.26% on a challenging test set. The results of our thyroid nodule segmentation experiments show that StableSeg GANs have flexibility to segment nodules more accurately than either comparable supervised segmentation models or uncontrolled GANs.
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Affiliation(s)
- Alisa Kunapinun
- Industrial Systems Engineering Department, Asian Institute of Technology, Pathumthani, Thailand
| | - Matthew N Dailey
- Information and Communication Technologies, Asian Institute of Technology, Pathumthani, Thailand
| | - Dittapong Songsaeng
- Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Manukid Parnichkun
- Industrial Systems Engineering Department, Asian Institute of Technology, Pathumthani, Thailand
| | | | - Mongkol Ekpanyapong
- Industrial Systems Engineering Department, Asian Institute of Technology, Pathumthani, Thailand.
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Bukasa JK, Bayauli-Mwasa P, Mbunga BK, Bangolo A, Kavula W, Mukaya J, Bindingija J, M’Buyamba-Kabangu JR. The Spectrum of Thyroid Nodules at Kinshasa University Hospital, Democratic Republic of Congo: A Cross-Sectional Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16203. [PMID: 36498276 PMCID: PMC9737877 DOI: 10.3390/ijerph192316203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/07/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
We analyzed the spectrum of thyroid nodules in patients attending the endocrinology unit care of the Kinshasa University Hospital and assessed their associated factors. We conducted a cross-sectional study, performing descriptive statistics and logistic regression. From the 888 enrolled patients, thyroid nodules were detected in 658 patients (74.1%), as mononodules in 22.5% and multiple nodules in 77.5%. Thyroid function was normal in 71.3% cases, while hyperthyroidism and hypothyroidism were found in 26.1% and 2.6% of cases, respectively. Women were more affected than men (75.1% vs. 63.6%; p = 0.03). Patients with thyroid nodules were older (44 ± 12 vs. 38 ± 12 years; p < 0.001), with a family history of goiter (38.3% vs. 27.4%; p = 0.003) and residence in the iodine-deficient region (51.7% vs. 38.8%; p = 0.012); they had a higher proportion of longer delays to consultation (47% vs. 20%; p < 0.001), but a higher rate of normal thyroid function (85.5% vs. 3 1.3%; p < 0.001). Thyroid nodules were associated with the delay to consultation (for duration ≥ three years, OR: 6.560 [95% CI: 3.525−12.208)], multiparity (present vs. absent: 2.863 [1.475−5.557]) and family history of goiter (present vs. absent: 2.086 [95% CI:1.231−3.534]) in female patients alone. The high frequency of thyroid nodules observed requires measures aimed at early detection in the population, the training of doctors involved in the management and the strengthening of technical platforms in our hospitals.
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Affiliation(s)
- John Kakamba Bukasa
- Endocrinology Unit, Department of Internal Medicine, University of Kinshasa Hospital, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
- Department of Endocrinology, Liège University Hospital Center, 4000 Liège, Belgium
| | - Pascal Bayauli-Mwasa
- Endocrinology Unit, Department of Internal Medicine, University of Kinshasa Hospital, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Branly Kilola Mbunga
- Kinshasa School of Public Health, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Ayrton Bangolo
- Department of Internal Medicine, Hackensack University Medical Center/Palisades Medical Center, North Bergen, NJ 07047, USA
| | - Wivine Kavula
- Kinshasa School of Public Health, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Jean Mukaya
- Radiology and Medical Imaging Unit, Department of Internal Medicine, University Hospital of Kinshasa, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Joseph Bindingija
- Endocrinology Unit, Department of Internal Medicine, University of Kinshasa Hospital, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Jean-René M’Buyamba-Kabangu
- Cardiology Unit, Department of Internal Medicine, University of Kinshasa Hospital, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
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Snider EJ, Hernandez-Torres SI, Avital G, Boice EN. Evaluation of an Object Detection Algorithm for Shrapnel and Development of a Triage Tool to Determine Injury Severity. J Imaging 2022; 8:jimaging8090252. [PMID: 36135417 PMCID: PMC9501864 DOI: 10.3390/jimaging8090252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/07/2022] [Accepted: 09/12/2022] [Indexed: 01/25/2023] Open
Abstract
Emergency medicine in austere environments rely on ultrasound imaging as an essential diagnostic tool. Without extensive training, identifying abnormalities such as shrapnel embedded in tissue, is challenging. Medical professionals with appropriate expertise are limited in resource-constrained environments. Incorporating artificial intelligence models to aid the interpretation can reduce the skill gap, enabling identification of shrapnel, and its proximity to important anatomical features for improved medical treatment. Here, we apply a deep learning object detection framework, YOLOv3, for shrapnel detection in various sizes and locations with respect to a neurovascular bundle. Ultrasound images were collected in a tissue phantom containing shrapnel, vein, artery, and nerve features. The YOLOv3 framework, classifies the object types and identifies the location. In the testing dataset, the model was successful at identifying each object class, with a mean Intersection over Union and average precision of 0.73 and 0.94, respectively. Furthermore, a triage tool was developed to quantify shrapnel distance from neurovascular features that could notify the end user when a proximity threshold is surpassed, and, thus, may warrant evacuation or surgical intervention. Overall, object detection models such as this will be vital to compensate for lack of expertise in ultrasound interpretation, increasing its availability for emergency and military medicine.
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Affiliation(s)
- Eric J. Snider
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | | | - Guy Avital
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- Trauma & Combat Medicine Branch, Surgeon General’s Headquarters, Israel Defense Forces, Ramat-Gan 52620, Israel
- Division of Anesthesia, Intensive Care & Pain Management, Tel-Aviv Sourasky Medical Center, Affiliated with the Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 64239, Israel
| | - Emily N. Boice
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- Correspondence: ; Tel.: +1-210-539-8721
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Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.
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Boice EN, Hernandez-Torres SI, Snider EJ. Comparison of Ultrasound Image Classifier Deep Learning Algorithms for Shrapnel Detection. J Imaging 2022; 8:jimaging8050140. [PMID: 35621904 PMCID: PMC9144026 DOI: 10.3390/jimaging8050140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 02/06/2023] Open
Abstract
Ultrasound imaging is essential in emergency medicine and combat casualty care, oftentimes used as a critical triage tool. However, identifying injuries, such as shrapnel embedded in tissue or a pneumothorax, can be challenging without extensive ultrasonography training, which may not be available in prolonged field care or emergency medicine scenarios. Artificial intelligence can simplify this by automating image interpretation but only if it can be deployed for use in real time. We previously developed a deep learning neural network model specifically designed to identify shrapnel in ultrasound images, termed ShrapML. Here, we expand on that work to further optimize the model and compare its performance to that of conventional models trained on the ImageNet database, such as ResNet50. Through Bayesian optimization, the model’s parameters were further refined, resulting in an F1 score of 0.98. We compared the proposed model to four conventional models: DarkNet-19, GoogleNet, MobileNetv2, and SqueezeNet which were down-selected based on speed and testing accuracy. Although MobileNetv2 achieved a higher accuracy than ShrapML, there was a tradeoff between accuracy and speed, with ShrapML being 10× faster than MobileNetv2. In conclusion, real-time deployment of algorithms such as ShrapML can reduce the cognitive load for medical providers in high-stress emergency or miliary medicine scenarios.
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10
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An image classification deep-learning algorithm for shrapnel detection from ultrasound images. Sci Rep 2022; 12:8427. [PMID: 35589931 PMCID: PMC9117994 DOI: 10.1038/s41598-022-12367-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/06/2022] [Indexed: 01/01/2023] Open
Abstract
Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce.
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11
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Zhang L, Zhuang Y, Hua Z, Han L, Li C, Chen K, Peng Y, Lin J. Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 29:75-90. [PMID: 33136086 DOI: 10.3233/xst-200775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.
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Affiliation(s)
- Ling Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Yan Zhuang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Zhan Hua
- China-Japan Friendship Hospital, Beijing, China
| | - Lin Han
- College of Biomedical Engineering, Sichuan University, Chengdu, China.,Highong Intellimage Medical Technology Tianjin Co., Ltd, Tianjin, China
| | - Cheng Li
- China-Japan Friendship Hospital, Beijing, China
| | - Ke Chen
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Yulan Peng
- West China Hospital of Sichuan University, Chengdu, China
| | - Jiangli Lin
- College of Biomedical Engineering, Sichuan University, Chengdu, China
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