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Moser F, Muller S, Lie T, Langø T, Hoff M. Automated segmentation of the median nerve in patients with carpal tunnel syndrome. Sci Rep 2024; 14:16757. [PMID: 39033223 PMCID: PMC11271291 DOI: 10.1038/s41598-024-65840-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/24/2024] [Indexed: 07/23/2024] Open
Abstract
Machine learning and deep learning are novel methods which are revolutionizing medical imaging. In our study we trained an algorithm with a U-Net shaped network to recognize ultrasound images of the median nerve in the complete distal half of the forearm and to measure the cross-sectional area at the inlet of the carpal tunnel. Images of 25 patient hands with carpal tunnel syndrome (CTS) and 26 healthy controls were recorded on a video loop covering 15 cm of the distal forearm and 2355 images were manually segmented. We found an average Dice score of 0.76 between manual and automated segmentation of the median nerve in its complete course, while the measurement of the cross-sectional area at the carpal tunnel inlet resulted in a 10.9% difference between manually and automated measurements. We regard this technology as a suitable device for verifying the diagnosis of CTS.
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Affiliation(s)
- Florentin Moser
- Department of Rheumatology, St. Olavs Hospital, Trondheim, Norway.
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Sébastien Muller
- Department of Health Research, SINTEF Digital, Trondheim, Norway
| | - Torgrim Lie
- Department of Health Research, SINTEF Digital, Trondheim, Norway
| | - Thomas Langø
- Department of Health Research, SINTEF Digital, Trondheim, Norway
- Department of Research, St. Olavs Hospital, Trondheim, Norway
| | - Mari Hoff
- Department of Rheumatology, St. Olavs Hospital, Trondheim, Norway
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
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2
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Renaud M, Gette M, Delpierre A, Calle S, Levassort F, Denis F, Rochefort GY. Intraoral Ultrasonography for the Exploration of Periodontal Tissues: A Technological Leap for Oral Diagnosis. Diagnostics (Basel) 2024; 14:1335. [PMID: 39001225 PMCID: PMC11240584 DOI: 10.3390/diagnostics14131335] [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: 05/14/2024] [Revised: 06/04/2024] [Accepted: 06/18/2024] [Indexed: 07/16/2024] Open
Abstract
INTRODUCTION Periodontal disease is an infectious syndrome presenting inflammatory aspects. Radiographic evaluation is an essential complement to clinical assessment but has limitations such as the impossibility of assessing tissue inflammation. It seems essential to consider new exploration methods in clinical practice. Ultrasound of periodontal tissues could make it possible to visualize periodontal structures and detect periodontal diseases (periodontal pocket measurement and the presence of intra-tissue inflammation). Clinical Innovation Report: An ultrasound probe has been specially developed to explore periodontal tissues. The objective of this clinical innovation report is to present this device and expose its potential. DISCUSSION Various immediate advantages favor using ultrasound: no pain, no bleeding, faster execution time, and an image recording that can be replayed without having to probe the patient again. Ultrasound measurements of pocket depth appear to be as reliable and reproducible as those obtained by manual probing, as do tissue thickness measurements and the detection of intra-tissue inflammation. CONCLUSIONS Ultrasound seems to have a broad spectrum of indications. Given the major advances offered by ultrasound imaging as a complementary aid to diagnosis, additional studies are necessary to validate these elements and clarify the potential field of application of ultrasound imaging in dentistry.
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Affiliation(s)
- Matthieu Renaud
- Faculty of Odontology, Tours University, 37000 Tours, France; (M.G.); (A.D.); (F.D.); (G.Y.R.)
- Department of Medicine and Bucco-Dental Surgery, Tours University Hospital, 37000 Tours, France
- Bioengineering Biomodulation and Imaging of the Orofacial Sphere, 2Bios, Odontology Department, Tours University, 37000 Tours, France
- N2C U1069 INSERM, Tours University, 37000 Tours, France
| | - Mickael Gette
- Faculty of Odontology, Tours University, 37000 Tours, France; (M.G.); (A.D.); (F.D.); (G.Y.R.)
- Bioengineering Biomodulation and Imaging of the Orofacial Sphere, 2Bios, Odontology Department, Tours University, 37000 Tours, France
| | - Alexis Delpierre
- Faculty of Odontology, Tours University, 37000 Tours, France; (M.G.); (A.D.); (F.D.); (G.Y.R.)
- Department of Medicine and Bucco-Dental Surgery, Tours University Hospital, 37000 Tours, France
- Bioengineering Biomodulation and Imaging of the Orofacial Sphere, 2Bios, Odontology Department, Tours University, 37000 Tours, France
| | - Samuel Calle
- GREMAN, Université de Tours, CNRS, INSA-CVL, 26 Rue Pierre et Marie Curie, 37100 Tours, France; (S.C.); (F.L.)
| | - Franck Levassort
- GREMAN, Université de Tours, CNRS, INSA-CVL, 26 Rue Pierre et Marie Curie, 37100 Tours, France; (S.C.); (F.L.)
| | - Frédéric Denis
- Faculty of Odontology, Tours University, 37000 Tours, France; (M.G.); (A.D.); (F.D.); (G.Y.R.)
- Department of Medicine and Bucco-Dental Surgery, Tours University Hospital, 37000 Tours, France
- EA 75-05 Education, Ethique, Santé, Faculté de Médecine, Université François-Rabelais, 37000 Tours, France
| | - Gaël Y. Rochefort
- Faculty of Odontology, Tours University, 37000 Tours, France; (M.G.); (A.D.); (F.D.); (G.Y.R.)
- Bioengineering Biomodulation and Imaging of the Orofacial Sphere, 2Bios, Odontology Department, Tours University, 37000 Tours, France
- iBrain U1253 INSEM, Tours University, 37000 Tours, France
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3
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Miguel OX, Kaczmarek E, Lee I, Ducharme R, Dingwall-Harvey ALJ, Rennicks White R, Bonin B, Aviv RI, Hawken S, Armour CM, Dick K, Walker MC. Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis. Sci Rep 2024; 14:9013. [PMID: 38641713 PMCID: PMC11031588 DOI: 10.1038/s41598-024-59248-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 04/08/2024] [Indexed: 04/21/2024] Open
Abstract
Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models' predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications. The study dataset consisted of 969 ultrasound images from unique patients; 646 control images and 323 cases of kidney anomalies, including 259 cases of unilateral urinary tract dilation and 64 cases of unilateral multicystic dysplastic kidney. The best performing model achieved a cross-validated area under the ROC curve of 91.28% ± 0.52%, with an overall accuracy of 84.03% ± 0.76%, sensitivity of 77.39% ± 1.99%, and specificity of 87.35% ± 1.28%. Our findings emphasize the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.
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Affiliation(s)
- Olivier X Miguel
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Emily Kaczmarek
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Inok Lee
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Alysha L J Dingwall-Harvey
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Ruth Rennicks White
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Obstetrics and Gynecology, University of Ottawa, 501 Smyth Road, Ottawa, ON, K1H-8L6, Canada
| | - Brigitte Bonin
- Department of Obstetrics and Gynecology, University of Ottawa, 501 Smyth Road, Ottawa, ON, K1H-8L6, Canada
- Department of Obstetrics, Gynecology and Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | - Richard I Aviv
- Department of Radiology and Medical Imaging, University of Ottawa, Ottawa, Canada
- Department of Radiology and Medical Imaging, The Ottawa Hospital, Ottawa, Canada
- Neuroscience Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- ICES, Toronto, Canada
| | - Christine M Armour
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- Department of Pediatrics, University of Ottawa, Ottawa, Canada
- Prenatal Screening Ontario, Better Outcomes Registry and Network, Ottawa, Canada
| | - Kevin Dick
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Mark C Walker
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada.
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada.
- Department of Pediatrics, University of Ottawa, Ottawa, Canada.
- Department of Obstetrics and Gynecology, University of Ottawa, 501 Smyth Road, Ottawa, ON, K1H-8L6, Canada.
- International and Global Health Office, University of Ottawa, Ottawa, Canada.
- BORN Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada.
- Department of Obstetrics, Gynecology and Newborn Care, The Ottawa Hospital, Ottawa, Canada.
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Waldner MJ, Strobel D. Ultrasound Diagnosis of Hepatocellular Carcinoma: Is the Future Defined by Artificial Intelligence? ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024; 45:8-12. [PMID: 38301631 DOI: 10.1055/a-2171-2674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Affiliation(s)
| | - Deike Strobel
- Medical Clinic 1, Erlangen University Hospital, Erlangen, Germany
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5
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Villa-Camacho JC, Baikpour M, Chou SHS. Artificial Intelligence for Breast US. JOURNAL OF BREAST IMAGING 2023; 5:11-20. [PMID: 38416959 DOI: 10.1093/jbi/wbac077] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Indexed: 03/01/2024]
Abstract
US is a widely available, commonly used, and indispensable imaging modality for breast evaluation. It is often the primary imaging modality for the detection and diagnosis of breast cancer in low-resource settings. In addition, it is frequently employed as a supplemental screening tool via either whole breast handheld US or automated breast US among women with dense breasts. In recent years, a variety of artificial intelligence systems have been developed to assist radiologists with the detection and diagnosis of breast lesions on US. This article reviews the background and evidence supporting the use of artificial intelligence tools for breast US, describes implementation strategies and impact on clinical workflow, and discusses potential emerging roles and future directions.
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Affiliation(s)
| | - Masoud Baikpour
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
| | - Shinn-Huey S Chou
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
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6
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Dicle O. Artificial intelligence in diagnostic ultrasonography. Diagn Interv Radiol 2023; 29:40-45. [PMID: 36959754 PMCID: PMC10679601 DOI: 10.4274/dir.2022.211260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/28/2022] [Indexed: 01/15/2023]
Abstract
Artificial intelligence (AI) continues to change paradigms in the field of medicine with new applications that are applicable to daily life. The field of ultrasonography, which has been developing since the 1950s and continues to be one of the most powerful tools in the field of diagnosis, is also the subject of AI studies, despite its unique problems. It is predicted that many operations, such as appropriate diagnostic tool selection, use of the most relevant parameters, improvement of low-quality images, automatic lesion detection and diagnosis from the image, and classification of pathologies, will be performed using AI tools in the near future. Especially with the use of convolutional neural networks, successful results can be obtained for lesion detection, segmentation, and classification from images. In this review, relevant developments are summarized based on the literature, and examples of the tools used in the field are presented.
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Affiliation(s)
- Oğuz Dicle
- Department of Radiology, Dokuz Eylül University Faculty of Medicine, İzmir, Turkey
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Lee S, Kang M, Byeon K, Lee SE, Lee IH, Kim YA, Kang SW, Park JT. Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features. J Digit Imaging 2022; 35:1091-1100. [PMID: 35411524 PMCID: PMC9582094 DOI: 10.1007/s10278-022-00625-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 03/24/2022] [Accepted: 03/26/2022] [Indexed: 11/27/2022] Open
Abstract
Although ultrasound plays an important role in the diagnosis of chronic kidney disease (CKD), image interpretation requires extensive training. High operator variability and limited image quality control of ultrasound images have made the application of computer-aided diagnosis (CAD) challenging. This study assessed the effect of integrating computer-extracted measurable features with the convolutional neural network (CNN) on the ultrasound image CAD accuracy of CKD. Ultrasound images from patients who visited Severance Hospital and Gangnam Severance Hospital in South Korea between 2011 and 2018 were used. A Mask regional CNN model was used for organ segmentation and measurable feature extraction. Data on kidney length and kidney-to-liver echogenicity ratio were extracted. The ResNet18 model classified kidney ultrasound images into CKD and non-CKD. Experiments were conducted with and without the input of the measurable feature data. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). A total of 909 patients (mean age, 51.4 ± 19.3 years; 414 [49.5%] men and 495 [54.5%] women) were included in the study. The average AUROC from the model trained using ultrasound images achieved a level of 0.81. Image training with the integration of automatically extracted kidney length and echogenicity features revealed an improved average AUROC of 0.88. This value further increased to 0.91 when the clinical information of underlying diabetes was also included in the model trained with CNN and measurable features. The automated step-wise machine learning-aided model segmented, measured, and classified the kidney ultrasound images with high performance. The integration of computer-extracted measurable features into the machine learning model may improve CKD classification.
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Affiliation(s)
- Sangmi Lee
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Korea
| | | | | | - Sang Eun Lee
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Biostatics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - In Ho Lee
- AI Team, INFINYX, Daegu, Republic of Korea
| | - Young Ah Kim
- Department of Medical Informatics, Yonsei University Health System, Seoul, Korea
| | - Shin-Wook Kang
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Korea
| | - Jung Tak Park
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Korea.
- Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
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8
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Automated prediction of early spontaneous miscarriage based on the analyzing ultrasonographic gestational sac imaging by the convolutional neural network: a case-control and cohort study. BMC Pregnancy Childbirth 2022; 22:621. [PMID: 35932003 PMCID: PMC9354356 DOI: 10.1186/s12884-022-04936-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 07/21/2022] [Indexed: 11/28/2022] Open
Abstract
Background It is challenging to predict the outcome of the pregnancy when fetal heart activity is detected in early pregnancy. However, an accurate prediction is of importance for obstetricians as it helps to provide appropriate consultancy and determine the frequency of ultrasound examinations. The purpose of this study was to investigate the role of the convolutional neural network (CNN) in the prediction of spontaneous miscarriage risk through the analysis of early ultrasound gestational sac images. Methods A total of 2196 ultrasound images from 1098 women with early singleton pregnancies of gestational age between 6 and 8 weeks were used for training a CNN for the prediction of the miscarriage in the retrospective study. The patients who had positive fetal cardiac activity on their first ultrasound but then experienced a miscarriage were enrolled. The control group was randomly selected in the same database from the fetuses confirmed to be normal during follow-up. Diagnostic performance of the algorithm was validated and tested in two separate test sets of 136 patients with 272 images, respectively. Performance in prediction of the miscarriage was compared between the CNN and the manual measurement of ultrasound characteristics in the prospective study. Results The accuracy of the predictive model was 80.32% and 78.1% in the retrospective and prospective study, respectively. The area under the receiver operating characteristic curve (AUC) for classification was 0.857 (95% confidence interval [CI], 0.793–0.922) in the retrospective study and 0.885 (95%CI, 0.846–0.925) in the prospective study, respectively. Correspondingly, the predictive power of the CNN was higher compared with manual ultrasound characteristics, for which the AUCs of the crown-rump length combined with fetal heart rate was 0.687 (95%CI, 0.587–0.775). Conclusions The CNN model showed high accuracy for predicting miscarriage through the analysis of early pregnancy ultrasound images and achieved better performance than that of manual measurement. Supplementary Information The online version contains supplementary material available at 10.1186/s12884-022-04936-0.
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Walker MC, Willner I, Miguel OX, Murphy MSQ, El-Chaâr D, Moretti F, Dingwall Harvey ALJ, Rennicks White R, Muldoon KA, Carrington AM, Hawken S, Aviv RI. Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester. PLoS One 2022; 17:e0269323. [PMID: 35731736 PMCID: PMC9216531 DOI: 10.1371/journal.pone.0269323] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 05/19/2022] [Indexed: 11/30/2022] Open
Abstract
Objective To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. Methods All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class activation heat maps (Grad-CAM) were generated to assess model interpretability. Results The dataset included 289 sagittal fetal ultrasound images;129 cystic hygroma cases and 160 normal NT controls. Overall model accuracy was 93% (95% CI: 88–98%), sensitivity 92% (95% CI: 79–100%), specificity 94% (95% CI: 91–96%), and the area under the ROC curve 0.94 (95% CI: 0.89–1.0). Grad-CAM heat maps demonstrated that the model predictions were driven primarily by the fetal posterior cervical area. Conclusions Our findings demonstrate that deep-learning algorithms can achieve high accuracy in diagnostic interpretation of cystic hygroma in the first trimester, validated against expert clinical assessment.
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Affiliation(s)
- Mark C. Walker
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- International and Global Health Office, University of Ottawa, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
- BORN Ontario, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- * E-mail:
| | - Inbal Willner
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | - Olivier X. Miguel
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Malia S. Q. Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Darine El-Chaâr
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | - Felipe Moretti
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | | | - Ruth Rennicks White
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
| | - Katherine A. Muldoon
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - André M. Carrington
- Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
- Department of Radiology and Medical Imaging, University of Ottawa, Ottawa, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Richard I. Aviv
- Department of Radiology and Medical Imaging, University of Ottawa, Ottawa, Canada
- Department of Radiology and Medical Imaging, The Ottawa Hospital, Ottawa, Canada
- Neuroscience Program, Ottawa Hospital Research Institute, Ottawa, Canada
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10
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Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities. SENSORS 2022; 22:s22124570. [PMID: 35746352 PMCID: PMC9228529 DOI: 10.3390/s22124570] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 12/13/2022]
Abstract
A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.
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Tarabichi M, Demetter P, Craciun L, Maenhaut C, Detours V. Thyroid cancer under the scope of emerging technologies. Mol Cell Endocrinol 2022; 541:111491. [PMID: 34740746 DOI: 10.1016/j.mce.2021.111491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 10/08/2021] [Accepted: 10/18/2021] [Indexed: 01/03/2023]
Abstract
The vast majority of thyroid cancers originate from follicular cells. We outline outstanding issues at each step along the path of cancer patient care, from prevention to post-treatment follow-up and highlight how emerging technologies will help address them in the coming years. Three directions will dominate the coming technological landscape. Genomics will reveal tumoral evolutionary history and shed light on how these cancers arise from the normal epithelium and the genomics alteration driving their progression. Transcriptomics will gain cellular and spatial resolution providing a full account of intra-tumor heterogeneity and opening a window on the microenvironment supporting thyroid tumor growth. Artificial intelligence will set morphological analysis on an objective quantitative ground laying the foundations of a systematic thyroid tumor classification system. It will also integrate into unified representations the molecular and morphological perspectives on thyroid cancer.
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Affiliation(s)
- Maxime Tarabichi
- Institute of Interdisciplinary Research (IRIBHM), Université Libre de Bruxelles, Brussels, Belgium.
| | - Pieter Demetter
- Department of Pathology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Ligia Craciun
- Department of Pathology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Carine Maenhaut
- Institute of Interdisciplinary Research (IRIBHM), Université Libre de Bruxelles, Brussels, Belgium.
| | - Vincent Detours
- Institute of Interdisciplinary Research (IRIBHM), Université Libre de Bruxelles, Brussels, Belgium.
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12
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Yu JS. ULTRASONOGRAPHY: changes in editorial policy necessary to lead global trends. Ultrasonography 2021; 41:1-3. [PMID: 34844293 PMCID: PMC8696147 DOI: 10.14366/usg.21244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 12/17/2022] Open
Affiliation(s)
- Jeong-Sik Yu
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea
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13
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Chambara N, Liu SYW, Lo X, Ying M. Comparative Analysis of Computer-Aided Diagnosis and Computer-Assisted Subjective Assessment in Thyroid Ultrasound. Life (Basel) 2021; 11:life11111148. [PMID: 34833024 PMCID: PMC8621517 DOI: 10.3390/life11111148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Abstract
The value of computer-aided diagnosis (CAD) and computer-assisted techniques equipped with different TIRADS remains ambiguous. Parallel diagnosis performances of computer-assisted subjective assessments and CAD were compared based on AACE, ATA, EU, and KSThR TIRADS. CAD software computed the diagnosis of 162 thyroid nodule sonograms. Two raters (R1 and R2) independently rated the sonographic features of the nodules using an online risk calculator while blinded to pathology results. Diagnostic efficiency measures were calculated based on the final pathology results. R1 had higher diagnostic performance outcomes than CAD with similarities between KSThR (SEN: 90.3% vs. 83.9%, p = 0.57; SPEC: 46% vs. 51%, p = 0.21; AUROC: 0.76 vs. 0.67, p = 0.02), and EU (SEN: 85.5% vs. 79%, p = 0.82; SPEC: 62% vs. 55%, p = 0.27; AUROC: 0.74 vs. 0.67, p = 0.06). Similarly, R2 had higher AUROC and specificity but lower sensitivity than CAD (KSThR-AUROC: 0.74 vs. 0.67, p = 0.13; SPEC: 61% vs. 46%, p = 0.02 and SEN: 75.8% vs. 83.9%, p = 0.31, and EU-AUROC: 0.69 vs. 0.67, p = 0.57, SPEC: 64% vs. 55%, p = 0.19, and SEN: 71% vs. 79%, p = 0.51, respectively). CAD had higher sensitivity but lower specificity than both R1 and R2 with AACE for 114 specified nodules (SEN: 92.5% vs. 88.7%, p = 0.50; 92.5% vs. 79.3%, p = 0.02, and SPEC: 26.2% vs. 54.1%, p = 0.001; 26.2% vs. 62.3%, p < 0.001, respectively). All diagnostic performance outcomes were comparable for ATA with 96 specified nodules. Computer-assisted subjective interpretation using KSThR is more ideal for ruling out papillary thyroid carcinomas than CAD. Future larger multi-center and multi-rater prospective studies with a diverse representation of thyroid cancers are necessary to validate these findings.
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Affiliation(s)
- Nonhlanhla Chambara
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;
| | - Shirley Yuk Wah Liu
- Department of Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China;
| | - Xina Lo
- Department of Surgery, North District Hospital, Sheung Shui, New Territories, Hong Kong, China;
| | - Michael Ying
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;
- Correspondence: ; Tel.: +852-3400-8566
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Yu JS. ULTRASONOGRAPHY: changes in submission and publication patterns 1 year after being listed in SCIE. Ultrasonography 2020; 40:1-2. [PMID: 33242933 PMCID: PMC7758102 DOI: 10.14366/usg.20184] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 11/20/2020] [Indexed: 12/15/2022] Open
Affiliation(s)
- Jeong-Sik Yu
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea
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