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Zhou B, Liu J, Yang Y, Ye X, Liu Y, Mao M, Sun X, Cui X, Zhou Q. Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning. BMC Cancer 2024; 24:810. [PMID: 38972977 PMCID: PMC11229345 DOI: 10.1186/s12885-024-12546-6] [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: 10/31/2023] [Accepted: 06/20/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND AND AIMS The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatures to predict the recurrence in PTC. METHODS A total of 554 patients with PTC who underwent ultrasound imaging before total thyroidectomy were included. Among them, 79 experienced at least one recurrence. Then 388 were divided into the training cohort and 166 into the validation cohort. The radiomics features were extracted from the region of interest (ROI) we manually drew on the tumor image. The feature selection was conducted using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. And multivariate Cox regression analysis was used to build the combined nomogram using radiomics signatures and significant clinicopathological characteristics. The efficiency of the nomogram was evaluated by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to analyze the recurrence-free survival (RFS) in different radiomics scores (Rad-scores) and risk scores. RESULTS The combined nomogram demonstrated the best performance and achieved an area under the curve (AUC) of 0.851 (95% CI: 0.788 to 0.913) in comparison to that of the radiomics signature and the clinical model in the training cohort at 3 years. In the validation cohort, the combined nomogram (AUC = 0.885, 95% CI: 0.805 to 0.930) also performed better. The calibration curves and DCA verified the clinical usefulness of combined nomogram. And the Kaplan-Meier analysis showed that in the training cohort, the cumulative RFS in patients with higher Rad-score was significantly lower than that in patients with lower Rad-score (92.0% vs. 71.9%, log rank P < 0.001), and the cumulative RFS in patients with higher risk score was significantly lower than that in patients with lower risk score (97.5% vs. 73.5%, log rank P < 0.001). In the validation cohort, patients with a higher Rad-score and a higher risk score also had a significantly lower RFS. CONCLUSION We proposed a nomogram combining clinicopathological variables and ultrasound radiomics signatures with excellent performance for recurrence prediction in PTC patients.
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Affiliation(s)
- Binqian Zhou
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Jianxin Liu
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Yaqin Yang
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Xuewei Ye
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Yang Liu
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Mingfeng Mao
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Xiaofeng Sun
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China.
| | - Qin Zhou
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China.
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Ferreira IJMCF, Simões JMS, Pereira B, Correia JNGCC, de Amaral Areia ALF. Ensemble learning for fetal ultrasound and maternal-fetal data to predict mode of delivery after labor induction. Sci Rep 2024; 14:15275. [PMID: 38961231 PMCID: PMC11222528 DOI: 10.1038/s41598-024-65394-6] [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: 01/09/2024] [Accepted: 06/19/2024] [Indexed: 07/05/2024] Open
Abstract
Providing adequate counseling on mode of delivery after induction of labor (IOL) is of utmost importance. Various AI algorithms have been developed for this purpose, but rely on maternal-fetal data, not including ultrasound (US) imaging. We used retrospectively collected clinical data from 808 subjects submitted to IOL, totaling 2024 US images, to train AI models to predict vaginal delivery (VD) and cesarean section (CS) outcomes after IOL. The best overall model used only clinical data (F1-score: 0.736; positive predictive value (PPV): 0.734). The imaging models employed fetal head, abdomen and femur US images, showing limited discriminative results. The best model used femur images (F1-score: 0.594; PPV: 0.580). Consequently, we constructed ensemble models to test whether US imaging could enhance the clinical data model. The best ensemble model included clinical data and US femur images (F1-score: 0.689; PPV: 0.693), presenting a false positive and false negative interesting trade-off. The model accurately predicted CS on 4 additional cases, despite misclassifying 20 additional VD, resulting in a 6.0% decrease in average accuracy compared to the clinical data model. Hence, integrating US imaging into the latter model can be a new development in assisting mode of delivery counseling.
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Affiliation(s)
- Iolanda João Mora Cruz Freitas Ferreira
- Faculty of Medicine of University of Coimbra, Obstetrics Department, University and Hospitalar Centre of Coimbra, Coimbra, Portugal.
- Maternidade Doutor Daniel de Matos, R. Miguel Torga, 3030-165, Coimbra, Portugal.
| | - Joana Maria Silva Simões
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
| | - Beatriz Pereira
- Department of Physics, University of Coimbra, Coimbra, Portugal
| | | | - Ana Luísa Fialho de Amaral Areia
- Faculty of Medicine of University of Coimbra, Obstetrics Department, University and Hospitalar Centre of Coimbra, Coimbra, Portugal
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Jacobs E, Wainman B, Bowness J. Applying artificial intelligence to the use of ultrasound as an educational tool: A focus on ultrasound-guided regional anesthesia. ANATOMICAL SCIENCES EDUCATION 2024; 17:919-925. [PMID: 36880869 DOI: 10.1002/ase.2266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/10/2023] [Accepted: 02/25/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Emma Jacobs
- Department of Anaesthesia, Royal Gwent Hospital, Aneurin Bevan University Health Board, Newport, UK
| | - Bruce Wainman
- Education Program in Anatomy, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Science, McMaster University, Hamilton, Ontario, Canada
| | - James Bowness
- Department of Anaesthesia, Royal Gwent Hospital, Aneurin Bevan University Health Board, Newport, UK
- OxSTaR Center, Nuffield Division of Anaesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Drukker L. The Holy Grail of obstetric ultrasound: can artificial intelligence detect hard-to-identify fetal cardiac anomalies? ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 64:5-9. [PMID: 38949769 DOI: 10.1002/uog.27703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/18/2024] [Indexed: 07/02/2024]
Abstract
Linked article: This Editorial comments on articles by Day et al. and Taksøe‐Vester et al.
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Affiliation(s)
- L Drukker
- Women's Ultrasound, Department of Obstetrics and Gynecology, Rabin-Beilinson Medical Center, School of Medicine, Faculty of Medical and Health Sciences, Tel-Aviv University, Tel Aviv, Israel
- Oxford Maternal & Perinatal Health Institute (OMPHI), University of Oxford, Oxford, UK
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Rezaei B, Tay ZW, Mostufa S, Manzari ON, Azizi E, Ciannella S, Moni HEJ, Li C, Zeng M, Gómez-Pastora J, Wu K. Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications. NANOSCALE 2024; 16:11802-11824. [PMID: 38809214 DOI: 10.1039/d4nr01195c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Recent advancements in medical imaging have brought forth various techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound, each contributing to improved diagnostic capabilities. Most recently, magnetic particle imaging (MPI) has become a rapidly advancing imaging modality with profound implications for medical diagnostics and therapeutics. By directly detecting the magnetization response of magnetic tracers, MPI surpasses conventional imaging modalities in sensitivity and quantifiability, particularly in stem cell tracking applications. Herein, this comprehensive review explores the fundamental principles, instrumentation, magnetic nanoparticle tracer design, and applications of MPI, offering insights into recent advancements and future directions. Novel tracer designs, such as zinc-doped iron oxide nanoparticles (Zn-IONPs), exhibit enhanced performance, broadening MPI's utility. Spatial encoding strategies, scanning trajectories, and instrumentation innovations are elucidated, illuminating the technical underpinnings of MPI's evolution. Moreover, integrating machine learning and deep learning methods enhances MPI's image processing capabilities, paving the way for more efficient segmentation, quantification, and reconstruction. The potential of superferromagnetic iron oxide nanoparticle chains (SFMIOs) as new MPI tracers further advanced the imaging quality and expanded clinical applications, underscoring the promising future of this emerging imaging modality.
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Affiliation(s)
- Bahareh Rezaei
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Zhi Wei Tay
- National Institute of Advanced Industrial Science and Technology (AIST), Health and Medical Research Institute, Tsukuba, Ibaraki 305-8564, Japan
| | - Shahriar Mostufa
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Omid Nejati Manzari
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Ebrahim Azizi
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Stefano Ciannella
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Hur-E-Jannat Moni
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Changzhi Li
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Minxiang Zeng
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | | | - Kai Wu
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
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García-Mejido JA, Solis-Martín D, Martín-Morán M, Fernández-Conde C, Fernández-Palacín F, Sainz-Bueno JA. Applicability of Deep Learning to Dynamically Identify the Different Organs of the Pelvic Floor in the Midsagittal Plane. Int Urogynecol J 2024:10.1007/s00192-024-05841-0. [PMID: 38913129 DOI: 10.1007/s00192-024-05841-0] [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: 03/21/2024] [Accepted: 05/01/2024] [Indexed: 06/25/2024]
Abstract
INTRODUCTION AND HYPOTHESIS The objective was to create and validate the usefulness of a convolutional neural network (CNN) for identifying different organs of the pelvic floor in the midsagittal plane via dynamic ultrasound. METHODS This observational and prospective study included 110 patients. Transperineal ultrasound scans were performed by an expert sonographer of the pelvic floor. A video of each patient was made that captured the midsagittal plane of the pelvic floor at rest and the change in the pelvic structures during the Valsalva maneuver. After saving the captured videos, we manually labeled the different organs in each video. Three different architectures were tested-UNet, FPN, and LinkNet-to determine which CNN model best recognized anatomical structures. The best model was trained with the 86 cases for the number of epochs determined by the stop criterion via cross-validation. The Dice Similarity Index (DSI) was used for CNN validation. RESULTS Eighty-six patients were included to train the CNN and 24 to test the CNN. After applying the trained CNN to the 24 test videos, we did not observe any failed segmentation. In fact, we obtained a DSI of 0.79 (95% CI: 0.73 - 0.82) as the median of the 24 test videos. When we studied the organs independently, we observed differences in the DSI of each organ. The poorest DSIs were obtained in the bladder (0.71 [95% CI: 0.70 - 0.73]) and uterus (0.70 [95% CI: 0.68 - 0.74]), whereas the highest DSIs were obtained in the anus (0.81 [95% CI: 0.80 - 0.86]) and levator ani muscle (0.83 [95% CI: 0.82 - 0.83]). CONCLUSIONS Our results show that it is possible to apply deep learning using a trained CNN to identify different pelvic floor organs in the midsagittal plane via dynamic ultrasound.
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Affiliation(s)
- José Antonio García-Mejido
- Department of Obstetrics and Gynecology, Valme University Hospital, Seville, Spain.
- Department of Surgery, Faculty of Medicine, University of Seville, Seville, Spain.
| | - David Solis-Martín
- Department of Computer Science and Artificial Intelligence, Faculty of Mathematics, University of Seville, Seville, Spain
| | - Marina Martín-Morán
- Department of Obstetrics and Gynecology, Valme University Hospital, Seville, Spain
| | | | | | - José Antonio Sainz-Bueno
- Department of Obstetrics and Gynecology, Valme University Hospital, Seville, Spain
- Department of Surgery, Faculty of Medicine, University of Seville, Seville, Spain
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Zhang J, Dawkins A. Artificial Intelligence in Ultrasound Imaging: Where Are We Now? Ultrasound Q 2024; 40:93-97. [PMID: 38842384 DOI: 10.1097/ruq.0000000000000680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Affiliation(s)
- Jie Zhang
- From the Department of Radiology, University of Kentucky, Lexington, KY
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Matschl J, Gembruch U, Strizek B, Recker F. Shaping the future of obstetric/gynecological ultrasound training. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 63:717-722. [PMID: 38031232 DOI: 10.1002/uog.27554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 12/01/2023]
Affiliation(s)
- J Matschl
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
| | - U Gembruch
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
| | - B Strizek
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
| | - F Recker
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
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Bai J, Lu Y, Liu H, He F, Guo X. Editorial: New technologies improve maternal and newborn safety. FRONTIERS IN MEDICAL TECHNOLOGY 2024; 6:1372358. [PMID: 38872737 PMCID: PMC11169838 DOI: 10.3389/fmedt.2024.1372358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 05/17/2024] [Indexed: 06/15/2024] Open
Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
| | - Huishu Liu
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Fang He
- Department of Obstetrics and Gynecology, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaohui Guo
- Department of Obstetrics, Shenzhen People’s Hospital, Shenzhen, China
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Zhao L, Huang J, Bell MAL, Raghavan P. Measuring myofascial shear strain in chronic shoulder pain with ultrasound shear strain imaging: a case report. BMC Musculoskelet Disord 2024; 25:412. [PMID: 38802774 PMCID: PMC11129449 DOI: 10.1186/s12891-024-07514-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 05/11/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Dysfunctional gliding of deep fascia and muscle layers forms the basis of myofascial pain and dysfunction, which can cause chronic shoulder pain. Ultrasound shear strain imaging may offer a non-invasive tool to quantitatively evaluate the extent of muscular dysfunctional gliding and its correlation with pain. This case study is the first to use ultrasound shear strain imaging to report the shear strain between the pectoralis major and minor muscles in shoulders with and without chronic pain. CASE PRESENTATION The shear strain between the pectoralis major and minor muscles during shoulder rotation in a volunteer with chronic shoulder pain was measured with ultrasound shear strain imaging. The results show that the mean ± standard deviation shear strain was 0.40 ± 0.09 on the affected side, compared to 1.09 ± 0.18 on the unaffected side (p<0.05). The results suggest that myofascial dysfunction may cause the muscles to adhere together thereby reducing shear strain on the affected side. CONCLUSION Our findings elucidate a potential pathophysiology of myofascial dysfunction in chronic shoulder pain and reveal the potential utility of ultrasound imaging to provide a useful biomarker for shear strain evaluation between the pectoralis major and minor muscles.
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Affiliation(s)
- Lingyi Zhao
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - Jonny Huang
- Department of Physical Medicine and Rehabilitation and Neurology, Johns Hopkins Medicine, Baltimore, MD, 21287, USA
| | - Muyinatu A Lediju Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, 21218, MD, USA.
| | - Preeti Raghavan
- Department of Physical Medicine and Rehabilitation and Neurology, Johns Hopkins Medicine, Baltimore, MD, 21287, USA.
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Ginsberg GM, Drukker L, Pollak U, Brezis M. Cost-utility analysis of prenatal diagnosis of congenital cardiac diseases using deep learning. COST EFFECTIVENESS AND RESOURCE ALLOCATION 2024; 22:44. [PMID: 38773527 PMCID: PMC11110271 DOI: 10.1186/s12962-024-00550-3] [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: 02/23/2024] [Accepted: 04/24/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Deep learning (DL) is a new technology that can assist prenatal ultrasound (US) in the detection of congenital heart disease (CHD) at the prenatal stage. Hence, an economic-epidemiologic evaluation (aka Cost-Utility Analysis) is required to assist policymakers in deciding whether to adopt the new technology. METHODS The incremental cost-utility ratios (CUR), of adding DL assisted ultrasound (DL-US) to the current provision of US plus pulse oximetry (POX), was calculated by building a spreadsheet model that integrated demographic, economic epidemiological, health service utilization, screening performance, survival and lifetime quality of life data based on the standard formula: CUR = Increase in Intervention Costs - Decrease in Treatment costs Averted QALY losses of adding DL to US & POX US screening data were based on real-world operational routine reports (as opposed to research studies). The DL screening cost of 145 USD was based on Israeli US costs plus 20.54 USD for reading and recording screens. RESULTS The addition of DL assisted US, which is associated with increased sensitivity (95% vs 58.1%), resulted in far fewer undiagnosed infants (16 vs 102 [or 2.9% vs 15.4%] of the 560 and 659 births, respectively). Adoption of DL-US will add 1,204 QALYs. with increased screening costs 22.5 million USD largely offset by decreased treatment costs (20.4 million USD). Therefore, the new DL-US technology is considered "very cost-effective", costing only 1,720 USD per QALY. For most performance combinations (sensitivity > 80%, specificity > 90%), the adoption of DL-US is either cost effective or very cost effective. For specificities greater than 98% (with sensitivities above 94%), DL-US (& POX) is said to "dominate" US (& POX) by providing more QALYs at a lower cost. CONCLUSION Our exploratory CUA calculations indicate the feasibility of DL-US as being at least cost-effective.
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Affiliation(s)
- Gary M Ginsberg
- Braun School of Public Health, Hebrew University, Jerusalem, Israel.
- HECON, Health Economics Consultancy, Jerusalem, Israel.
| | - Lior Drukker
- Department of Obstetrics and Gynecology, Rabin-Belinson Medical Center, Petah Tikva, Israel
- School of Medicine, Faculty of Medical and Health Sciences, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Uri Pollak
- Pediatric Critical Care Sector, Hadassah University Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University Medical Center, Jerusalem, Israel
| | - Mayer Brezis
- Braun School of Public Health, Hebrew University, Jerusalem, Israel
- Center for Quality and Safety, Hadassah University Medical Center, Jerusalem, Israel
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Kraus B, Harrison G, Santos R, Vils Pedersen MR. Ultrasound education across European Federation of Radiographers Societies (EFRS) countries: Similarities and differences. Radiography (Lond) 2024; 30:715-722. [PMID: 38428195 DOI: 10.1016/j.radi.2024.01.024] [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: 10/13/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 03/03/2024]
Abstract
INTRODUCTION Ultrasound education varies greatly across European healthcare systems. This paper focuses on ultrasound academic education as a part of wider suite of surveys on radiographers working in ultrasound. The aim was to investigate sonography educational levels, methods of training, course duration and other factors in European Federation of Radiographers Societies (EFRS) member countries. METHOD In 2019 an online survey was sent to the 38 EFRS member societies to distribute to higher education institutions within their own country. The survey was in English and contained different types of questions such as closed questions, free text options, and scale responses, to investigate sonography education including academic course types and duration, curriculum content, learning and teaching methods. RESULTS A total of 45 responses were received, showing wide variation in the duration of training between the respective countries. Academic level 7 (part-time) ultrasound education was most frequently reported (n = 13), followed by direct entry ultrasound courses (n = 9) and bachelor's degree programmes at EQF level 6 (n = 7). The duration of part-time courses ranged from nine months up to four years. CONCLUSION Sonography training and education varies among EFRS member countries ranging from short focused courses to postgraduate awards. Few countries offer sonography education leading to an award. The majority of clinical teaching and learning takes place in the learner's workplace. IMPLICATIONS FOR PRACTICE High quality academic and clinical education for radiographers extending their role into ultrasound is important to ensure safe, effective sonography practice and good patient care.
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Affiliation(s)
- B Kraus
- European Federation of Radiographer Societies, Churchilllaan 11, 3527 GV Utrecht, the Netherlands; Department of Health Sciences, Radiological Technology, University of Applied Sciences FH Campus Wien, Favoritenstrasse 226, A-1100 Vienna, Austria.
| | - G Harrison
- European Federation of Radiographer Societies, Churchilllaan 11, 3527 GV Utrecht, the Netherlands; Society and College of Radiographers, 207 Providence Square Mill Street, London SE1 2EW, UK; School of Health and Psychological Sciences. City, University of London, Northampton Square, London, EC1V 0HB, UK
| | - R Santos
- European Federation of Radiographer Societies, Churchilllaan 11, 3527 GV Utrecht, the Netherlands; Medical Imaging Radiotherapy Department, Coimbra Health School, Polytechnic Institute of Coimbra, Rue 5 de Outubro, 3046-854, Portugal
| | - M R Vils Pedersen
- European Federation of Radiographer Societies, Churchilllaan 11, 3527 GV Utrecht, the Netherlands; University Hospital Southern Denmark, Department of Radiology, Vejle Hospital, Beriderbakken 4, 7100 Vejle, Denmark; University of Southern Denmark, Institute of Regional Health, Campusvej 55, Odense, Denmark
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Fukuzawa F, Yanagita Y, Yokokawa D, Uchida S, Yamashita S, Li Y, Shikino K, Tsukamoto T, Noda K, Uehara T, Ikusaka M. Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study. JMIR MEDICAL EDUCATION 2024; 10:e52674. [PMID: 38602313 PMCID: PMC11024399 DOI: 10.2196/52674] [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: 09/12/2023] [Revised: 01/31/2024] [Accepted: 02/15/2024] [Indexed: 04/12/2024]
Abstract
Background Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. Objective This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. Methods Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. Results ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included. Conclusions Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.
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Affiliation(s)
- Fumitoshi Fukuzawa
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Yasutaka Yanagita
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Daiki Yokokawa
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Shun Uchida
- Uchida Internal Medicine Clinic, Saitama-shi, Japan
| | - Shiho Yamashita
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Yu Li
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Kiyoshi Shikino
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Tomoko Tsukamoto
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Kazutaka Noda
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Takanori Uehara
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Masatomi Ikusaka
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
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Wei NY, Li XK, Lu XD, Liu XT, Sun RJ, Wang Y. Study on the Consistency Between Automatic Measurement Based on Convolutional Neural Network Technology and Manual Visual Evaluation in Intracavitary Ultrasonic Cine of Anterior Pelvic. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:671-681. [PMID: 38185941 DOI: 10.1002/jum.16392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024]
Abstract
OBJECTIVES This study was to evaluate the application of automatic measurement based on convolutional neural network (CNN) technology in intracavitary ultrasound cine of anterior pelvic. METHODS A total of 500 patients who underwent pelvic floor ultrasound examination at Peking University Shenzhen Hospital from July 2021 to February 2022 were retrospectively retrieved by the picture archiving and communication system (PACS) system, and 300 cases were used as a training set. The training set was labeled by three experienced ultrasound physicians to train CNN models and develop an automatic measurement software. The remaining 200 cases were used as a test set. Automatic measurement software identified relevant anatomical structures frame by frame and determined the two frames with the greatest difference, calculated the bladder neck descent (BND), urethral rotation angle (URA), and retrovesical angle (RA). Meanwhile, two experienced ultrasound physicians evaluated the resting frame and the maximum Valsalva frame on the cines by manual visual evaluation, labeled the anatomical structures in the corresponding frame, such as the inferoposterior margin of pubic symphysis, the mid-axis of pubic symphysis, bladder contour, and urethra in the front, and calculated BND, URA, and RA. Considering that the residual urine volume (RUV) in the bladder may affect the results, enrolled patients were grouped according to the RUV (10-50 mL, 50-100 mL, and >100 mL). The consistency of the results by automatic measurement and manual visual evaluation was evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman graph. RESULTS Of the 200 cases in the test set, 120 cases were successfully identified by the CNN automatic software with a 60% recognition rate. In the case of successful identification, the ICC of manual visual evaluation measurement and automatic measurement was 0.936 (BND), 0.911 (URA), 0.756 (RA in rest), and 0.877 (RA at maximum Valsalva), respectively. In addition, the RUV had a negligible effect on the consistency. The Bland-Altman plot shows the proportion of samples outside the limit was below 5%. CONCLUSIONS CNN-based automatic measurement software exhibited high reliability in anterior pelvic measurement, which results in a significantly enhanced measurement efficiency.
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Affiliation(s)
- Ni-Ya Wei
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China
| | - Xiao-Kun Li
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China
| | - Xi-Duo Lu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China
- Shenzhen RayShape Medical Technology Co., Ltd, Shenzhen, China
| | - Xin-Ting Liu
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China
| | - Rui-Jie Sun
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China
| | - Yue Wang
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China
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15
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Li P, Xiong F, Huang X, Wen X. Construction and optimization of vending machine decision support system based on improved C4.5 decision tree. Heliyon 2024; 10:e25024. [PMID: 38318033 PMCID: PMC10838796 DOI: 10.1016/j.heliyon.2024.e25024] [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: 11/02/2023] [Revised: 12/14/2023] [Accepted: 01/18/2024] [Indexed: 02/07/2024] Open
Abstract
The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment.
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Affiliation(s)
- Ping Li
- School of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, China
| | - Fang Xiong
- School of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, China
| | - Xibei Huang
- School of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, China
| | - Xiaojun Wen
- School of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, China
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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [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: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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17
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Deslandes A, Avery J, Chen H, Leonardi M, Condous G, Hull ML. Artificial intelligence as a teaching tool for gynaecological ultrasound: A systematic search and scoping review. Australas J Ultrasound Med 2024; 27:5-11. [PMID: 38434541 PMCID: PMC10902831 DOI: 10.1002/ajum.12368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Abstract
Purpose The aim of this study was to investigate the current application of artificial intelligence (AI) tools in the teaching of ultrasound skills as they pertain to gynaecological ultrasound. Methods A scoping review was performed. Eight databases (MEDLINE, EMBASE, EMCARE, CINAHL, Scopus, Web of Science, IEEE Xplore and ACM digital library) were searched in December 2022 using predefined keywords. All types of publications were eligible for inclusion so long as they reported the use of an AI tool, included reference to or discussion of teaching or the improvement of ultrasound skills and pertained to gynaecological ultrasound. Conference abstracts and non-English language papers which could not be adequately translated into English were excluded. Results The initial database search returned 481 articles. After screening against our inclusion and exclusion criteria, two were deemed to meet the inclusion criteria. Neither of the articles included reported original research (one systematic review and one review article). Neither of the included articles explicitly provided details of specific tools developed for the teaching of ultrasound skills for gynaecological imaging but highlighted similar applications within the field of obstetrics which could potentially be expanded. Conclusion Artificial intelligence can potentially assist in the training of sonographers and other ultrasound operators, including in the field of gynaecological ultrasound. This scoping review revealed however that to date, no original research has been published reporting the use or development of such a tool specifically for gynaecological ultrasound.
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Affiliation(s)
- Alison Deslandes
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - Jodie Avery
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - Hsiang‐Ting Chen
- School of Computer and Mathematical SciencesUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - Mathew Leonardi
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
- Department of Obstetrics and GynecologyMcMaster UniversityHamiltonOntarioCanada
| | - George Condous
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - M. Louise Hull
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
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18
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Skelton E, Webb R, Malamateniou C, Rutherford M, Ayers S. The impact of antenatal imaging on parent experience and prenatal attachment: a systematic review. J Reprod Infant Psychol 2024; 42:22-44. [PMID: 35736666 DOI: 10.1080/02646838.2022.2088710] [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: 10/31/2021] [Accepted: 06/04/2022] [Indexed: 10/17/2022]
Abstract
INTRODUCTION Medical imaging in pregnancy (antenatal imaging) is routine. However, the effect of seeing fetal images on the parent-fetal relationship is not well understood, particularly for fathers or partners, or when using advanced imaging technologies. This review aimed to explore how parent experience and prenatal attachment is impacted by antenatal imaging. METHOD Database searches were performed between September 2020 and April 2021 Inclusion criteria were English language primary research studies published since 2000, describing or reporting measures of attachment after antenatal imaging in expectant parents. The Pillar Integration Process was used for integrative synthesis. FINDINGS Twenty-three studies were included. Six pillar themes were developed: 1) the scan experience begins before the scan appointment; 2) the scan as a pregnancy ritual; 3) feeling actively involved in the scan; 4) parents' priorities for knowledge and understanding of the scan change during pregnancy; 5) the importance of the parent-sonographer partnership during scanning; and 6) scans help to create a social identity for the unborn baby. CONCLUSION Antenatal imaging can enhance prenatal attachment. Parents value working collaboratively with sonographers to be actively involved in the experience. Sonographers can help facilitate attachment by delivering parent-centred care tailored to parents' emotional and knowledge needs.
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Affiliation(s)
- Emily Skelton
- Division of Radiography and Midwifery, City University of London, London, UK
| | - Rebecca Webb
- Centre for Maternal and Child Health Research, City University of London, London, UK
| | | | | | - Susan Ayers
- Centre for Maternal and Child Health Research, City University of London, London, UK
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19
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Creswell L, Rolnik DL, Lindow SW, O’Gorman N. Preterm Birth: Screening and Prediction. Int J Womens Health 2023; 15:1981-1997. [PMID: 38146587 PMCID: PMC10749552 DOI: 10.2147/ijwh.s436624] [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: 08/24/2023] [Accepted: 12/13/2023] [Indexed: 12/27/2023] Open
Abstract
Preterm birth (PTB) affects approximately 10% of births globally each year and is the most significant direct cause of neonatal death and of long-term disability worldwide. Early identification of women at high risk of PTB is important, given the availability of evidence-based, effective screening modalities, which facilitate decision-making on preventative strategies, particularly transvaginal sonographic cervical length (CL) measurement. There is growing evidence that combining CL with quantitative fetal fibronectin (qfFN) and maternal risk factors in the extensively peer-reviewed and validated QUanititative Innovation in Predicting Preterm birth (QUiPP) application can aid both the triage of patients who present as emergencies with symptoms of preterm labor and high-risk asymptomatic women attending PTB surveillance clinics. The QUiPP app risk of delivery thus supports shared decision-making with patients on the need for increased outpatient surveillance, in-patient treatment for preterm labor or simply reassurance for those unlikely to deliver preterm. Effective triage of patients at preterm gestations is an obstetric clinical priority as correctly timed administration of antenatal corticosteroids will maximise their neonatal benefits. This review explores the predictive capacity of existing predictive tests for PTB in both singleton and multiple pregnancies, including the QUiPP app v.2. and discusses promising new research areas, which aim to predict PTB through cervical stiffness and elastography measurements, metabolomics, extracellular vesicles and artificial intelligence.
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Affiliation(s)
- Lyndsay Creswell
- Department of Obstetrics and Gynecology, The Coombe Hospital, Dublin, Ireland
| | - Daniel Lorber Rolnik
- Department of Obstetrics and Gynecology, Monash University, Melbourne, VIC, Australia
| | - Stephen W Lindow
- Department of Obstetrics and Gynecology, The Coombe Hospital, Dublin, Ireland
| | - Neil O’Gorman
- Department of Obstetrics and Gynecology, The Coombe Hospital, Dublin, Ireland
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20
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Jost E, Kosian P, Jimenez Cruz J, Albarqouni S, Gembruch U, Strizek B, Recker F. Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology. J Clin Med 2023; 12:6833. [PMID: 37959298 PMCID: PMC10649694 DOI: 10.3390/jcm12216833] [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: 09/21/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.
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Affiliation(s)
- Elena Jost
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Philipp Kosian
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Jorge Jimenez Cruz
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Shadi Albarqouni
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz AI, Helmholtz Munich, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Ulrich Gembruch
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Brigitte Strizek
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Florian Recker
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
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21
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Calcaterra V, Pagani V, Zuccotti G. Maternal and fetal health in the digital twin era. Front Pediatr 2023; 11:1251427. [PMID: 37900683 PMCID: PMC10601630 DOI: 10.3389/fped.2023.1251427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/03/2023] [Indexed: 10/31/2023] Open
Affiliation(s)
- Valeria Calcaterra
- Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
- Pediatric Department, Buzzi Children’s Hospital, Milano, Italy
| | - Valter Pagani
- Grant & Research Department-LJA-2021, Asomi College of Sciences, Marsa, Malta
| | - Gianvincenzo Zuccotti
- Pediatric Department, Buzzi Children’s Hospital, Milano, Italy
- Department of Biomedical and Clinical Science, University of Milano, Milano, Italy
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22
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Cembrowska-Lech D, Krzemińska A, Miller T, Nowakowska A, Adamski C, Radaczyńska M, Mikiciuk G, Mikiciuk M. An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture. BIOLOGY 2023; 12:1298. [PMID: 37887008 PMCID: PMC10603917 DOI: 10.3390/biology12101298] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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Affiliation(s)
- Danuta Cembrowska-Lech
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
| | - Adrianna Krzemińska
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | - Tymoteusz Miller
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
| | - Anna Nowakowska
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
| | - Cezary Adamski
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | | | - Grzegorz Mikiciuk
- Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Małgorzata Mikiciuk
- Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
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23
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Grisolia G, Pinto A. Smart ICV™ versus VOCAL™ in fetal brain volume assessment: Can we begin to trust artificial intelligence in clinical practice? JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:1152-1154. [PMID: 37431153 DOI: 10.1002/jcu.23521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/12/2023]
Abstract
This paper is a commentary about an interesting research conducted with the aim of testing the agreement between manual versus automatic technique in measuring fetal brain volume. Given the high degree of reliability between the two techniques, we hope that the new automatic software can become useful tools in identifying fetuses with reduced brain volume at high risk of adverse neurological outcome.
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Affiliation(s)
- Gianpaolo Grisolia
- Department of Obstetrics and Gynecology, Carlo Poma Hospital, Mantua, Italy
| | - Alessia Pinto
- Department of Obstetrics and Gynecology, Carlo Poma Hospital, Mantua, Italy
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24
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Tang J, Liang Y, Jiang Y, Liu J, Zhang R, Huang D, Pang C, Huang C, Luo D, Zhou X, Li R, Zhang K, Xie B, Hu L, Zhu F, Xia H, Lu L, Wang H. A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography. NPJ Digit Med 2023; 6:143. [PMID: 37573426 PMCID: PMC10423245 DOI: 10.1038/s41746-023-00883-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 07/21/2023] [Indexed: 08/14/2023] Open
Abstract
Duct-dependent congenital heart diseases (CHDs) are a serious form of CHD with a low detection rate, especially in underdeveloped countries and areas. Although existing studies have developed models for fetal heart structure identification, there is a lack of comprehensive evaluation of the long axis of the aorta. In this study, a total of 6698 images and 48 videos are collected to develop and test a two-stage deep transfer learning model named DDCHD-DenseNet for screening critical duct-dependent CHDs. The model achieves a sensitivity of 0.973, 0.843, 0.769, and 0.759, and a specificity of 0.985, 0.967, 0.956, and 0.759, respectively, on the four multicenter test sets. It is expected to be employed as a potential automatic screening tool for hierarchical care and computer-aided diagnosis. Our two-stage strategy effectively improves the robustness of the model and can be extended to screen for other fetal heart development defects.
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Affiliation(s)
- Jiajie Tang
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- School of Information Management, Wuhan University, Wuhan, China
| | - Yongen Liang
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yuxuan Jiang
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- School of Information Management, Wuhan University, Wuhan, China
| | - Jinrong Liu
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Rui Zhang
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Danping Huang
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Chengcheng Pang
- Cardiovascular Pediatrics/Guangdong Cardiovascular Institute/Medical Big Data Center, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Chen Huang
- Department of Medical Ultrasonics/Shenzhen Longgang Maternal and Child Health Hospital, Shenzhen, China
| | - Dongni Luo
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Xue Zhou
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Ruizhuo Li
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- School of Medicine, Southern China University of Technology, Guangzhou, China
| | - Kanghui Zhang
- School of Information Management, Wuhan University, Wuhan, China
| | - Bingbing Xie
- School of Information Management, Wuhan University, Wuhan, China
| | - Lianting Hu
- Cardiovascular Pediatrics/Guangdong Cardiovascular Institute/Medical Big Data Center, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Fanfan Zhu
- School of Information Management, Wuhan University, Wuhan, China
| | - Huimin Xia
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
| | - Long Lu
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
- School of Information Management, Wuhan University, Wuhan, China.
- Center for Healthcare Big Data Research, The Big Data Institute, Wuhan University, Wuhan, China.
- School of Public Health, Wuhan University, Wuhan, China.
| | - Hongying Wang
- Department of Medical Ultrasonics/Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
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Suhag A, Kidd J, McGath M, Rajesh R, Gelfinbein J, Cacace N, Monteleone B, Chavez MR. ChatGPT: a pioneering approach to complex prenatal differential diagnosis. Am J Obstet Gynecol MFM 2023; 5:101029. [PMID: 37257586 DOI: 10.1016/j.ajogmf.2023.101029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/19/2023] [Indexed: 06/02/2023]
Abstract
This commentary examines how ChatGPT can assist healthcare teams in the prenatal diagnosis of rare and complex cases by creating a differential diagnoses based on deidentified clinical findings, while also acknowledging its limitations.
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Affiliation(s)
- Anju Suhag
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez).
| | - Jennifer Kidd
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez)
| | - Meghan McGath
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez); Department of Clinical Genetics, NYU Langone Hospital-Long Island, Mineola, NY (Mses McGath and Cacace, and Dr Monteleone)
| | - Raeshmma Rajesh
- Department of Obstetrics and Gynecology, Richmond University Medical Center, Staten Island, NY (Dr Rajesh)
| | | | - Nicole Cacace
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez); Department of Clinical Genetics, NYU Langone Hospital-Long Island, Mineola, NY (Mses McGath and Cacace, and Dr Monteleone)
| | - Berrin Monteleone
- Department of Clinical Genetics, NYU Langone Hospital-Long Island, Mineola, NY (Mses McGath and Cacace, and Dr Monteleone)
| | - Martin R Chavez
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Health, NYU Langone Hospital-Long Island, NYU Long Island School of Medicine, Mineola, NY (Drs Suhag and Kidd, Mses McGath and Cacace, and Dr Chavez)
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Ramirez Zegarra R, Ghi T. Use of artificial intelligence and deep learning in fetal ultrasound imaging. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:185-194. [PMID: 36436205 DOI: 10.1002/uog.26130] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/06/2022] [Accepted: 11/21/2022] [Indexed: 06/16/2023]
Abstract
Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep-learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep-learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal and abnormal fetal anatomy and measurement of fetal biometry. In this Review, we provide a comprehensive explanation of the fundamentals of deep learning in fetal imaging, with particular focus on its clinical applicability. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- R Ramirez Zegarra
- Department of Medicine and Surgery, Obstetrics and Gynecology Unit, University of Parma, Parma, Italy
| | - T Ghi
- Department of Medicine and Surgery, Obstetrics and Gynecology Unit, University of Parma, Parma, Italy
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Werner H, Santos IF, Giraldi GA, Lopes J, Ribeiro G, Lopes FP. Fetal magnetic resonance imaging artifacts: role of deep learning to improve imaging. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:302-303. [PMID: 36840982 DOI: 10.1002/uog.26185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Affiliation(s)
- H Werner
- Instituto de Ensino e Pesquisa, Dasa (IEPD), Brazil
- BiodesignLab Dasa/PUC-Rio, Rio de Janeiro, Brazil
| | - I Félix Santos
- Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil
| | - G A Giraldi
- Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil
| | - J Lopes
- BiodesignLab Dasa/PUC-Rio, Rio de Janeiro, Brazil
| | - G Ribeiro
- BiodesignLab Dasa/PUC-Rio, Rio de Janeiro, Brazil
| | - F P Lopes
- Instituto de Ensino e Pesquisa, Dasa (IEPD), Brazil
- BiodesignLab Dasa/PUC-Rio, Rio de Janeiro, Brazil
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Day TG, Matthew J, Budd S, Hajnal JV, Simpson JM, Razavi R, Kainz B. Sonographer interaction with artificial intelligence: collaboration or conflict? ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:167-174. [PMID: 37523514 DOI: 10.1002/uog.26238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/05/2023] [Accepted: 04/14/2023] [Indexed: 08/02/2023]
Affiliation(s)
- T G Day
- Department of Congenital Cardiology, Evelina London Children's Healthcare, Guy's and St Thomas' NHS Foundation Trust, London, UK
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - J Matthew
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - S Budd
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - J V Hajnal
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - J M Simpson
- Department of Congenital Cardiology, Evelina London Children's Healthcare, Guy's and St Thomas' NHS Foundation Trust, London, UK
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - R Razavi
- Department of Congenital Cardiology, Evelina London Children's Healthcare, Guy's and St Thomas' NHS Foundation Trust, London, UK
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - B Kainz
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
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Chavez MR, Butler TS, Rekawek P, Heo H, Kinzler WL. Chat Generative Pre-trained Transformer: why we should embrace this technology. Am J Obstet Gynecol 2023; 228:706-711. [PMID: 36924908 DOI: 10.1016/j.ajog.2023.03.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 03/17/2023]
Abstract
With the advent of artificial intelligence that not only can learn from us but also can communicate with us in plain language, humans are embarking on a brave new future. The interaction between humans and artificial intelligence has never been so widespread. Chat Generative Pre-trained Transformer is an artificial intelligence resource that has potential uses in the practice of medicine. As clinicians, we have the opportunity to help guide and develop new ways to use this powerful tool. Optimal use of any tool requires a certain level of comfort. This is best achieved by appreciating its power and limitations. Being part of the process is crucial in maximizing its use in our field. This clinical opinion demonstrates the potential uses of Chat Generative Pre-trained Transformer for obstetrician-gynecologists and encourages readers to serve as the driving force behind this resource.
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Affiliation(s)
- Martin R Chavez
- Division of Maternal-Fetal Medicine, Department of Obstetrics Gynecology, New York University Langone Hospital-Long Island, New York University Long Island School of Medicine, Mineola, NY.
| | - Thomas S Butler
- New York University Langone Reproductive Specialists of New York, New York University Langone Hospital-Long Island, New York University Langone Long Island School of Medicine, Mineola, New York
| | - Patricia Rekawek
- Division of Maternal-Fetal Medicine, Department of Obstetrics Gynecology, New York University Langone Hospital-Long Island, New York University Long Island School of Medicine, Mineola, NY
| | - Hye Heo
- Division of Maternal-Fetal Medicine, Department of Obstetrics Gynecology, New York University Langone Hospital-Long Island, New York University Long Island School of Medicine, Mineola, NY
| | - Wendy L Kinzler
- Division of Maternal-Fetal Medicine, Department of Obstetrics Gynecology, New York University Langone Hospital-Long Island, New York University Long Island School of Medicine, Mineola, NY
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Xiao S, Zhang J, Zhu Y, Zhang Z, Cao H, Xie M, Zhang L. Application and Progress of Artificial Intelligence in Fetal Ultrasound. J Clin Med 2023; 12:jcm12093298. [PMID: 37176738 PMCID: PMC10179567 DOI: 10.3390/jcm12093298] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 04/01/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.
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Affiliation(s)
- Sushan Xiao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Junmin Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ye Zhu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Zisang Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Haiyan Cao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Mingxing Xie
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Li Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
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Du Y, McNestry C, Wei L, Antoniadi AM, McAuliffe FM, Mooney C. Machine learning-based clinical decision support systems for pregnancy care: A systematic review. Int J Med Inform 2023; 173:105040. [PMID: 36907027 DOI: 10.1016/j.ijmedinf.2023.105040] [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: 07/08/2022] [Revised: 01/12/2023] [Accepted: 03/03/2023] [Indexed: 03/11/2023]
Abstract
BACKGROUND Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of vital importance, and machine learning-based CDSSs have shown positive impact on pregnancy care. OBJECTIVE This paper aims to investigate what has been done in CDSSs in the context of pregnancy care using machine learning, and what aspects require attention from future researchers. METHODS We conducted a systematic review of existing literature following a structured process of literature search, paper selection and filtering, and data extraction and synthesis. RESULTS 17 research papers were identified on the topic of CDSS development for different aspects of pregnancy care using various machine learning algorithms. We discovered an overall lack of explainability in the proposed models. We also observed a lack of experimentation, external validation and discussion around culture, ethnicity and race from the source data, with most studies using data from a single centre or country, and an overall lack of awareness of applicability and generalisability of the CDSSs regarding different populations. Finally, we found a gap between machine learning practices and CDSS implementation, and an overall lack of user testing. CONCLUSION Machine learning-based CDSSs are still under-explored in the context of pregnancy care. Despite the open problems that remain, the few studies that tested a CDSS for pregnancy care reported positive effects, reinforcing the potential of such systems to improve clinical practice. We encourage future researchers to take into consideration the aspects we identified in order for their work to translate into clinical use.
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Affiliation(s)
- Yuhan Du
- UCD School of Computer Science, University College Dublin, Dublin, Ireland
| | - Catherine McNestry
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Lan Wei
- UCD School of Computer Science, University College Dublin, Dublin, Ireland
| | | | - Fionnuala M McAuliffe
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Catherine Mooney
- UCD School of Computer Science, University College Dublin, Dublin, Ireland.
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Ahuja M, Sarkar A, Sharma V. Integrating Technologies: An Affordable Health Care System in Digital India. J Midlife Health 2023; 14:66-68. [PMID: 38029028 PMCID: PMC10664050 DOI: 10.4103/jmh.jmh_138_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 07/27/2023] [Accepted: 07/27/2023] [Indexed: 12/01/2023] Open
Affiliation(s)
- Maninder Ahuja
- Director, Ahuja Health Care Services, Faridabad, Haryana, New Delhi, India
- Founder President SMLM (Society of Meaningful Life Management), Faridabad, Haryana, New Delhi, India
| | - Avir Sarkar
- Department of Obstetrics and Gynecology, ESIC Medical College and Hospital, Faridabad, Haryana, New Delhi, India
| | - Vartika Sharma
- Department of Obstetrics and Gynecology, All India Institute of Medical Sciences, New Delhi, India E-mail:
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Ali S, Byamugisha J, Kawooya MG, Kakibogo IM, Ainembabazi I, Biira EA, Kagimu AN, Migisa A, Munyakazi M, Kuniha S, Scheele C, Papageorghiou AT, Klipstein-Grobusch K, Rijken MJ. Standardization and quality control of Doppler and fetal biometric ultrasound measurements in low-income setting. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 61:481-487. [PMID: 37011080 DOI: 10.1002/uog.26051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/27/2022] [Accepted: 08/01/2022] [Indexed: 06/19/2023]
Abstract
OBJECTIVE The aim of this study was to determine the quality of fetal biometry and pulsed-wave Doppler ultrasound measurements in a prospective cohort study in Uganda. METHODS This was an ancillary study of the Ending Preventable Stillbirths by Improving Diagnosis of Babies at Risk (EPID) project, in which women enroled in early pregnancy underwent Doppler and fetal biometric assessment at 32-40 weeks of gestation. Sonographers undertook 6 weeks of training followed by onsite refresher training and audit exercises. A total of 125 images for each of the umbilical artery (UA), fetal middle cerebral artery (MCA), left and right uterine arteries (UtA), head circumference (HC), abdominal circumference (AC) and femur length (FL) were selected randomly from the EPID study database and evaluated independently by two experts in a blinded fashion using objective scoring criteria. Inter-rater agreement was assessed using modified Fleiss' kappa for nominal variables and systematic errors were explored using quantile-quantile (Q-Q) plots. RESULTS For Doppler measurements, 96.8% of the UA images, 84.8% of the MCA images and 93.6% of the right UtA images were classified as of acceptable quality by both reviewers. For fetal biometry, 96.0% of the HC images, 96.0% of the AC images and 88.0% of the FL images were considered acceptable by both reviewers. The kappa values for inter-rater reliability of quality assessment were 0.94 (95% CI, 0.87-0.99) for the UA, 0.71 (95% CI, 0.58-0.82) for the MCA, 0.87 (95% CI, 0.78-0.95) for the right UtA, 0.94 (95% CI, 0.87-0.98) for the HC, 0.93 (95% CI, 0.87-0.98) for the AC and 0.78 (95% CI, 0.66-0.88) for the FL measurements. The Q-Q plots indicated no influence of systematic bias in the measurements. CONCLUSIONS Training local healthcare providers to perform Doppler ultrasound, and implementing quality control systems and audits using objective scoring tools in clinical and research settings, is feasible in low- and middle-income countries. Although we did not assess the impact of in-service retraining offered to practitioners deviating from prescribed standards, such interventions should enhance the quality of ultrasound measurements and should be investigated in future studies. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- S Ali
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
- Ernest Cook Ultrasound Research and Education Institute (ECUREI), Mengo Hospital, Kampala, Uganda
| | - J Byamugisha
- School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - M G Kawooya
- Ernest Cook Ultrasound Research and Education Institute (ECUREI), Mengo Hospital, Kampala, Uganda
| | - I M Kakibogo
- Antenatal and Maternity Unit, Kagadi Hospital, Kagadi District, Uganda
| | - I Ainembabazi
- Antenatal and Maternity Unit, Kagadi Hospital, Kagadi District, Uganda
| | | | - A N Kagimu
- Ernest Cook Ultrasound Research and Education Institute (ECUREI), Mengo Hospital, Kampala, Uganda
| | - A Migisa
- Ernest Cook Ultrasound Research and Education Institute (ECUREI), Mengo Hospital, Kampala, Uganda
| | - M Munyakazi
- Ernest Cook Ultrasound Research and Education Institute (ECUREI), Mengo Hospital, Kampala, Uganda
| | - S Kuniha
- Department of Radiology, Mulago Hospital Complex, Kampala, Uganda
| | - C Scheele
- Division of Woman and Baby, Department of Obstetrics, Birth Center Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - A T Papageorghiou
- Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - K Klipstein-Grobusch
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - M J Rijken
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Division of Woman and Baby, Department of Obstetrics, Birth Center Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
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Papastefanou I, Nicolaides KH, Salomon LJ. Audit of fetal biometry: understanding sources of error to improve our practice. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 61:431-435. [PMID: 36647209 DOI: 10.1002/uog.26156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/15/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Affiliation(s)
- I Papastefanou
- Fetal Medicine Research Institute, King's College Hospital, London, UK
- Department of Women and Children's Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - K H Nicolaides
- Fetal Medicine Research Institute, King's College Hospital, London, UK
| | - L J Salomon
- Department of Obstetrics, Fetal Medicine and Surgery, Necker-Enfants Malades Hospital, AP-HP, Paris, France
- URP FETUS 7328 and LUMIERE Platform, University of Paris Cité, Institut Imagine, Paris, France
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AI: Can It Make a Difference to the Predictive Value of Ultrasound Breast Biopsy? Diagnostics (Basel) 2023; 13:diagnostics13040811. [PMID: 36832299 PMCID: PMC9955683 DOI: 10.3390/diagnostics13040811] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/17/2023] [Accepted: 02/18/2023] [Indexed: 02/23/2023] Open
Abstract
(1) Background: This study aims to compare the ground truth (pathology results) against the BI-RADS classification of images acquired while performing breast ultrasound diagnostic examinations that led to a biopsy and against the result of processing the same images through the AI algorithm KOIOS DS TM (KOIOS). (2) Methods: All results of biopsies performed with ultrasound guidance during 2019 were recovered from the pathology department. Readers selected the image which better represented the BI-RADS classification, confirmed correlation to the biopsied image, and submitted it to the KOIOS AI software. The results of the BI-RADS classification of the diagnostic study performed at our institution were set against the KOIOS classification and both were compared to the pathology reports. (3) Results: 403 cases were included in this study. Pathology rendered 197 malignant and 206 benign reports. Four biopsies on BI-RADS 0 and two images are included. Of fifty BI-RADS 3 cases biopsied, only seven rendered cancers. All but one had a positive or suspicious cytology; all were classified as suspicious by KOIOS. Using KOIOS, 17 B3 biopsies could have been avoided. Of 347 BI-RADS 4, 5, and 6 cases, 190 were malignant (54.7%). Because only KOIOS suspicious and probably malignant categories should be biopsied, 312 biopsies would have resulted in 187 malignant lesions (60%), but 10 cancers would have been missed. (4) Conclusions: KOIOS had a higher ratio of positive biopsies in this selected case study vis-à-vis the BI-RADS 4, 5 and 6 categories. A large number of biopsies in the BI-RADS 3 category could have been avoided.
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Sarno L, Neola D, Carbone L, Saccone G, Carlea A, Miceli M, Iorio GG, Mappa I, Rizzo G, Girolamo RD, D'Antonio F, Guida M, Maruotti GM. Use of artificial intelligence in obstetrics: not quite ready for prime time. Am J Obstet Gynecol MFM 2023; 5:100792. [PMID: 36356939 DOI: 10.1016/j.ajogmf.2022.100792] [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: 07/18/2022] [Revised: 10/18/2022] [Accepted: 10/28/2022] [Indexed: 11/09/2022]
Abstract
Artificial intelligence is finding several applications in healthcare settings. This study aimed to report evidence on the effectiveness of artificial intelligence application in obstetrics. Through a narrative review of literature, we described artificial intelligence use in different obstetrical areas as follows: prenatal diagnosis, fetal heart monitoring, prediction and management of pregnancy-related complications (preeclampsia, preterm birth, gestational diabetes mellitus, and placenta accreta spectrum), and labor. Artificial intelligence seems to be a promising tool to help clinicians in daily clinical activity. The main advantages that emerged from this review are related to the reduction of inter- and intraoperator variability, time reduction of procedures, and improvement of overall diagnostic performance. However, nowadays, the diffusion of these systems in routine clinical practice raises several issues. Reported evidence is still very limited, and further studies are needed to confirm the clinical applicability of artificial intelligence. Moreover, better training of clinicians designed to use these systems should be ensured, and evidence-based guidelines regarding this topic should be produced to enhance the strengths of artificial systems and minimize their limits.
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Affiliation(s)
- Laura Sarno
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Daniele Neola
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida).
| | - Luigi Carbone
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Gabriele Saccone
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Annunziata Carlea
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Marco Miceli
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida); CEINGE Biotecnologie Avanzate, Naples, Italy (Dr Miceli)
| | - Giuseppe Gabriele Iorio
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Ilenia Mappa
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Rome Tor Vergata, Rome, Italy (Dr Mappa and Dr Rizzo)
| | - Giuseppe Rizzo
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Rome Tor Vergata, Rome, Italy (Dr Mappa and Dr Rizzo)
| | - Raffaella Di Girolamo
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Francesco D'Antonio
- Center for Fetal Care and High Risk Pregnancy, Department of Obstetrics and Gynecology, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy (Dr D'Antonio)
| | - Maurizio Guida
- Gynecology and Obstetrics Unit, Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Sarno, Dr Neola, Dr Carbone, Dr Saccone, Dr Carlea, Dr Miceli, Dr Iorio, Dr Girolamo, and Dr Guida)
| | - Giuseppe Maria Maruotti
- Gynecology and Obstetrics Unit, Department of Public Health, School of Medicine, University of Naples Federico II, Naples, Italy (Dr Maruotti)
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Malani SN, Shrivastava D, Raka MS. A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology. Cureus 2023; 15:e34891. [PMID: 36925982 PMCID: PMC10013256 DOI: 10.7759/cureus.34891] [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: 10/15/2022] [Accepted: 02/12/2023] [Indexed: 03/18/2023] Open
Abstract
The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.
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Affiliation(s)
- Sagar N Malani
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Deepti Shrivastava
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Mayur S Raka
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
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Bowness JS, Macfarlane AJ, Burckett-St Laurent D, Harris C, Margetts S, Morecroft M, Phillips D, Rees T, Sleep N, Vasalauskaite A, West S, Noble JA, Higham H. Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia. Br J Anaesth 2023; 130:226-233. [PMID: 36088136 PMCID: PMC9900732 DOI: 10.1016/j.bja.2022.07.049] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/26/2022] [Accepted: 07/14/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Ultrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation, including ultrasound. In this exploratory study, we evaluated ultrasound scanning performance by non-experts in ultrasound-guided regional anaesthesia, with and without the use of an assistive AI device. METHODS Twenty-one anaesthetists, all non-experts in ultrasound-guided regional anaesthesia, underwent a standardised teaching session in ultrasound scanning for six peripheral nerve blocks. All then performed a scan for each block; half of the scans were performed with AI assistance and half without. Experts assessed acquisition of the correct block view and correct identification of sono-anatomical structures on each view. Participants reported scan confidence, experts provided a global rating score of scan performance, and scans were timed. RESULTS Experts assessed 126 ultrasound scans. Participants acquired the correct block view in 56/62 (90.3%) scans with the device compared with 47/62 (75.1%) without (P=0.031, two data points lost). Correct identification of sono-anatomical structures on the view was 188/212 (88.8%) with the device compared with 161/208 (77.4%) without (P=0.002). There was no significant overall difference in participant confidence, expert global performance score, or scan time. CONCLUSIONS Use of an assistive AI device was associated with improved ultrasound image acquisition and interpretation. Such technology holds potential to augment performance of ultrasound scanning for regional anaesthesia by non-experts, potentially expanding patient access to these techniques. CLINICAL TRIAL REGISTRATION NCT05156099.
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Affiliation(s)
- James S. Bowness
- Oxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK,Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK,Corresponding author.
| | - Alan J.R. Macfarlane
- Department of Anaesthesia, Glasgow Royal Infirmary, Glasgow, UK,School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, UK
| | | | - Catherine Harris
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK
| | | | | | - David Phillips
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK
| | - Tom Rees
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK
| | | | | | - Simeon West
- Department of Anaesthesia, University College London, London, UK
| | - J. Alison Noble
- Institute of Biomedical Engineering, University of Oxford, UK
| | - Helen Higham
- Oxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK,Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Rizzo G, Patrizi L. Referral ultrasound in fetal medicine: May telemedicine play a pivotal role? JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:72-73. [PMID: 36468304 DOI: 10.1002/jcu.23276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 07/07/2022] [Indexed: 06/17/2023]
Affiliation(s)
- Giuseppe Rizzo
- Department of Obstetrics and Gynecology, Università di Roma Tor Vergata, Rome, Italy
| | - Lodovico Patrizi
- Department of Obstetrics and Gynecology, Università di Roma Tor Vergata, Rome, Italy
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Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis. iScience 2022; 26:105692. [PMID: 36570770 PMCID: PMC9771726 DOI: 10.1016/j.isci.2022.105692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/31/2022] [Accepted: 11/26/2022] [Indexed: 12/12/2022] Open
Abstract
The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames.
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Bai J, Sun Z, Yu S, Lu Y, Long S, Wang H, Qiu R, Ou Z, Zhou M, Zhi D, Zhou M, Jiang X, Chen G. A framework for computing angle of progression from transperineal ultrasound images for evaluating fetal head descent using a novel double branch network. Front Physiol 2022; 13:940150. [PMID: 36531181 PMCID: PMC9755498 DOI: 10.3389/fphys.2022.940150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 11/23/2022] [Indexed: 11/15/2023] Open
Abstract
Background: Accurate assessment of fetal descent by monitoring the fetal head (FH) station remains a clinical challenge in guiding obstetric management. Angle of progression (AoP) has been suggested to be a reliable and reproducible parameter for the assessment of FH descent. Methods: A novel framework, including image segmentation, target fitting and AoP calculation, is proposed for evaluating fetal descent. For image segmentation, this study presents a novel double branch segmentation network (DBSN), which consists of two parts: an encoding part receives image input, and a decoding part composed of deformable convolutional blocks and ordinary convolutional blocks. The decoding part includes the lower and upper branches, and the feature map of the lower branch is used as the input of the upper branch to assist the upper branch in decoding after being constrained by the attention gate (AG). Given an original transperineal ultrasound (TPU) image, areas of the pubic symphysis (PS) and FH are firstly segmented using the proposed DBSN, the ellipse contours of segmented regions are secondly fitted with the least square method, and three endpoints are finally determined for calculating AoP. Results: Our private dataset with 313 transperineal ultrasound (TPU) images was used for model evaluation with 5-fold cross-validation. The proposed method achieves the highest Dice coefficient (93.4%), the smallest Average Surface Distance (6.268 pixels) and the lowest AoP difference (5.993°) by comparing four state-of-the-art methods. Similar results (Dice coefficient: 91.7%, Average Surface Distance: 7.729 pixels: AoP difference: 5.110°) were obtained on a public dataset with >3,700 TPU images for evaluating its generalization performance. Conclusion: The proposed framework may be used for the automatic measurement of AoP with high accuracy and generalization performance. However, its clinical availability needs to be further evaluated.
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Affiliation(s)
- Jieyun Bai
- College of Information Science and Technology, Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
| | - Zhanhang Sun
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Sheng Yu
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Yaosheng Lu
- College of Information Science and Technology, Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
| | - Shun Long
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Ruiyu Qiu
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Zhanhong Ou
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Minghong Zhou
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Dengjiang Zhi
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Mengqiang Zhou
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Xiaosong Jiang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Gaowen Chen
- Obstetrics and Gynecology Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Drukker L, Sharma H, Karim JN, Droste R, Noble JA, Papageorghiou AT. Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep-learning-based analysis. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2022; 60:759-765. [PMID: 35726505 PMCID: PMC10107110 DOI: 10.1002/uog.24975] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/04/2022] [Accepted: 06/10/2022] [Indexed: 05/31/2023]
Abstract
OBJECTIVE Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan-acquisition order is not prescribed. Using deep-learning-based video analysis, the aim of this study was to develop a deeper understanding of the clinical workflow undertaken by sonographers during second-trimester anomaly scans. METHODS We collected prospectively full-length video recordings of routine second-trimester anomaly scans. Important scan events in the videos were identified by detecting automatically image freeze and image/clip save. The video immediately preceding and following the important event was extracted and labeled as one of 11 commonly acquired anatomical structures. We developed and used a purposely trained and tested deep-learning annotation model to label automatically the large number of scan events. Thus, anomaly scans were partitioned as a sequence of anatomical planes or fetal structures obtained over time. RESULTS A total of 496 anomaly scans performed by 14 sonographers were available for analysis. UK guidelines specify that an image or videoclip of five different anatomical regions must be stored and these were detected in the majority of scans: head/brain was detected in 97.2% of scans, coronal face view (nose/lips) in 86.1%, abdomen in 93.1%, spine in 95.0% and femur in 92.3%. Analyzing the clinical workflow, we observed that sonographers were most likely to begin their scan by capturing the head/brain (in 24.4% of scans), spine (in 23.2%) or thorax/heart (in 22.8%). The most commonly identified two-structure transitions were: placenta/amniotic fluid to maternal anatomy, occurring in 44.5% of scans; head/brain to coronal face (nose/lips) in 42.7%; abdomen to thorax/heart in 26.1%; and three-dimensional/four-dimensional face to sagittal face (profile) in 23.7%. Transitions between three or more consecutive structures in sequence were uncommon (up to 13% of scans). None of the captured anomaly scans shared an entirely identical sequence. CONCLUSIONS We present a novel evaluation of the anomaly scan acquisition process using a deep-learning-based analysis of ultrasound video. We note wide variation in the number and sequence of structures obtained during routine second-trimester anomaly scans. Overall, each anomaly scan was found to be unique in its scanning sequence, suggesting that sonographers take advantage of the fetal position and acquire the standard planes according to their visibility rather than following a strict acquisition order. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- L. Drukker
- Nuffield Department of Women's and Reproductive HealthJohn Radcliffe Hospital, University of OxfordOxfordUK
- Women's Ultrasound, Department of Obstetrics and GynecologyBeilinson Medical Center, Sackler Faculty of Medicine, Tel Aviv UniversityTel AvivIsrael
| | - H. Sharma
- Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
| | - J. N. Karim
- Nuffield Department of Women's and Reproductive HealthJohn Radcliffe Hospital, University of OxfordOxfordUK
| | - R. Droste
- Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
| | - J. A. Noble
- Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
| | - A. T. Papageorghiou
- Nuffield Department of Women's and Reproductive HealthJohn Radcliffe Hospital, University of OxfordOxfordUK
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Alsharid M, Drukker L, Sharma H, Noble JA, Papageorghiou AT. A picture is worth a thousand words: textual analysis of the routine 20-week scan. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2022; 60:710-711. [PMID: 35708528 DOI: 10.1002/uog.24972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 05/28/2022] [Accepted: 06/10/2022] [Indexed: 05/27/2023]
Affiliation(s)
- M Alsharid
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - L Drukker
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - H Sharma
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - J A Noble
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - A T Papageorghiou
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
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Imaging fetal anatomy. Semin Cell Dev Biol 2022; 131:78-92. [PMID: 35282997 DOI: 10.1016/j.semcdb.2022.02.023] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/23/2022] [Accepted: 02/23/2022] [Indexed: 02/07/2023]
Abstract
Due to advancements in ultrasound techniques, the focus of antenatal ultrasound screening is moving towards the first trimester of pregnancy. The early first trimester however remains in part, a 'black box', due to the size of the developing embryo and the limitations of contemporary scanning techniques. Therefore there is a need for images of early anatomical developmental to improve our understanding of this area. By using new imaging techniques, we can not only obtain better images to further our knowledge of early embryonic development, but clear images of embryonic and fetal development can also be used in training for e.g. sonographers and fetal surgeons, or to educate parents expecting a child with a fetal anomaly. The aim of this review is to provide an overview of the past, present and future techniques used to capture images of the developing human embryo and fetus and provide the reader newest insights in upcoming and promising imaging techniques. The reader is taken from the earliest drawings of da Vinci, along the advancements in the fields of in utero ultrasound and MR imaging techniques towards high-resolution ex utero imaging using Micro-CT and ultra-high field MRI. Finally, a future perspective is given about the use of artificial intelligence in ultrasound and new potential imaging techniques such as synchrotron radiation-based CT to increase our knowledge regarding human development.
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Multitask Deep Neural Network for the Fully Automatic Measurement of the Angle of Progression. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5192338. [PMID: 36092792 PMCID: PMC9462992 DOI: 10.1155/2022/5192338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 08/04/2022] [Indexed: 11/22/2022]
Abstract
The angle of progression (AoP) for assessing fetal head (FH) descent during labor is measured from the standard plane of transperineal ultrasound images as the angle between a line through the long axis of pubic symphysis (PS) and a second line from the right end of PS tangentially to the contour of the FH. This paper presents a multitask network with a shared feature encoder and three task-special decoders for standard plane recognition (Task1), image segmentation (Task2) of PS and FH, and endpoint detection (Task3) of PS. Based on the segmented FH and two endpoints of PS from standard plane images, we determined the right FH tangent point that passes through the right endpoint of PS and then computed the AoP using the above three points. In this paper, the efficient channel attention unit is introduced into the shared feature encoder for improving the robustness of layer region encoding, while an attention fusion module is used to promote cross-branch interaction between the encoder for Task2 and that for Task3, and a shape-constrained loss function is designed for enhancing the robustness to noise based on the convex shape-prior. We use Pearson's correlation coefficient and the Bland–Altman graph to assess the degree of agreement. The dataset includes 1964 images, where 919 images are nonstandard planes, and the other 1045 images are standard planes including PS and FH. We achieve a classification accuracy of 92.26%, and for the AoP calculation, an absolute mean (STD) value of the difference in AoP (∆AoP) is 3.898° (3.192°), the Pearson's correlation coefficient between manual and automated AoP was 0.964 and the Bland-Altman plot demonstrates they were statistically significant (P < 0.05). In conclusion, our approach can achieve a fully automatic measurement of AoP with good efficiency and may help labor progress in the future.
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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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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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48
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Shibata A. Point-of-care ultrasound for abdominal pain in obstetrics and gynecological diseases. J Med Ultrason (2001) 2022; 49:629-637. [PMID: 35689711 DOI: 10.1007/s10396-022-01218-2] [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: 08/15/2021] [Accepted: 03/29/2022] [Indexed: 11/25/2022]
Abstract
Ultrasound is a minimally invasive technique recommended for the evaluation of abdominal pain in young, premenopausal women and pregnant women. Ectopic pregnancy, ovarian cyst torsion, ovarian hemorrhage, myoma degeneration, and pyometra can be detected with point-of-care ultrasound (POCUS) in the case of acute abdominal pain. This article describes the utility of POCUS in females with abdominal pain in obstetrics and gynecological diseases.
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Affiliation(s)
- Ayako Shibata
- Obstetrics and Gynecology, Yodogawa Christian Hospital, 1-7-50 Kunijima, Higashiyodogawa-ku, Osaka, 533-0024, Japan.
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Zhou B, Yang X, Curran WJ, Liu T. Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:1329-1342. [PMID: 34467542 DOI: 10.1002/jum.15819] [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: 12/29/2020] [Revised: 08/01/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magnetic resonance imaging, QUS suffers from several major drawbacks: poor image quality and inter- and intra-observer variability. Therefore, there is a great need to develop automated methods to improve the image quality of QUS. In recent years, there has been increasing interest in artificial intelligence (AI) applications in medical imaging, and a large number of research studies in AI in QUS have been conducted. The purpose of this review is to describe and categorize recent research into AI applications in QUS. We first introduce the AI workflow and then discuss the various AI applications in QUS. Finally, challenges and future potential AI applications in QUS are discussed.
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Affiliation(s)
- Boran Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
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50
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Drukker L, Droste R, Ioannou C, Impey L, Noble JA, Papageorghiou AT. Function and Safety of SlowflowHD Ultrasound Doppler in Obstetrics. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:1157-1162. [PMID: 35300877 DOI: 10.1016/j.ultrasmedbio.2022.02.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 06/14/2023]
Abstract
SlowflowHD is a new ultrasound Doppler imaging technology that allows visualization of flow within small blood vessels. In this mode, a proprietary algorithm differentiates between low-speed flow and signals attributed to tissue motion so that microvessel vasculature can be examined. Our objectives were to describe the low-velocity Doppler mode principles, to assess the bone thermal index (TIb) safety parameter in obstetric ultrasound scans and to evaluate adherence to professional guidelines. To achieve the latter goals, we retrospectively reviewed prospectively collected ultrasound images and video clips from pregnancy ultrasound scans at >10 wk of gestation over 4 mo. We used a custom-built optical character recognition-based software to automatically identify all images and video clips using this technology and extract the TIb. Overall, a total of 185 ultrasound scans performed by three fetal medicine physicians were included, of which 60, 54 and 71 scans were first-, second- and third-trimester scans, respectively. The mean (highest recorded) TIb values were 0.32 (0.70), 0.23 (0.70) and 0.32 (0.60) in the first, second, and third trimesters, respectively. Thermal index values were within recommended values set by the World Federation for Ultrasound in Medicine and Biology American Institute of Ultrasound in Medicine and British Medical Ultrasound Society in all scans.
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Affiliation(s)
- Lior Drukker
- Women's Ultrasound, Department of Obstetrics and Gynecology, Beilinson Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Israel; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Richard Droste
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Christos Ioannou
- Fetal Medicine Unit, Department of Maternal and Fetal Medicine, Women's Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Lawrence Impey
- Fetal Medicine Unit, Department of Maternal and Fetal Medicine, Women's Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - J Alison Noble
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Aris T Papageorghiou
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, United Kingdom.
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