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Leaf MC, Musselman K, Wang KC. Cutting-edge care: unleashing artificial intelligence's potential in gynecologic surgery. Curr Opin Obstet Gynecol 2024; 36:255-259. [PMID: 38869434 DOI: 10.1097/gco.0000000000000971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is now integrated in our daily life. It has also been incorporated in medicine with algorithms to diagnose, recommend treatment options, and estimate prognosis. RECENT FINDINGS AI in surgery differs from virtual AI used for clinical application. Physical AI in the form of computer vision and augmented reality is used to improve surgeon's skills, performance, and patient outcomes. SUMMARY Several applications of AI and augmented reality are utilized in gynecologic surgery. AI's potential use can be found in all phases of surgery: preoperatively, intra-operatively, and postoperatively. Its current benefits are for improving accuracy, surgeon's precision, and reducing complications.
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Affiliation(s)
- Marie-Claire Leaf
- Gynecology and Obstetrics, Division of Minimally Invasive Gynecologic Surgery, Johns Hopkins Hospital, Baltimore, Maryland, USA
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Daykan Y, O'Reilly BA. The role of artificial intelligence in the future of urogynecology. Int Urogynecol J 2023; 34:1663-1666. [PMID: 37486359 DOI: 10.1007/s00192-023-05612-3] [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: 07/03/2023] [Accepted: 07/08/2023] [Indexed: 07/25/2023]
Abstract
Artificial intelligence (AI) in medicine is a rapidly growing field aimed at using machine learning models to improve health outcomes and patient experiences. Many new platforms have become accessible and therefore it seems inevitable that we consider how to implement them in our day-to-day practice. Currently, the specialty of urogynecology faces new challenges as the population grows, life expectancy increases, and quality of life expectation is much improved. As AI has a lot of potential to promote the discipline of urogynecology, we aim to explore its abilities and possible use in the future. Challenges and risks are associated with using AI, and a responsible use of such resources is required.
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Affiliation(s)
- Yair Daykan
- Department of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland.
- Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Barry A O'Reilly
- Department of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland
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3
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Seval MM, Varlı B. Current developments in artificial intelligence from obstetrics and gynecology to urogynecology. Front Med (Lausanne) 2023; 10:1098205. [PMID: 36910480 PMCID: PMC9995368 DOI: 10.3389/fmed.2023.1098205] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/09/2023] [Indexed: 02/25/2023] Open
Abstract
In today's medical practice clinicians need to struggle with a huge amount of data to improve the outcomes of the patients. Sometimes one clinician needs to deal with thousands of ultrasound images or hundred papers of laboratory results. To overcome this shortage, computers get in help of human beings and they are educated under the term "artificial intelligence." We were using artificial intelligence in our daily lives (i.e., Google, Netflix, etc.), but applications in medicine are relatively new. In obstetrics and gynecology, artificial intelligence models mostly use ultrasound images for diagnostic purposes but nowadays researchers started to use other medical recordings like non-stress tests or urodynamics study results to develop artificial intelligence applications. Urogynecology is a developing subspecialty of obstetrics and gynecology, and articles about artificial intelligence in urogynecology are limited but in this review, we aimed to increase clinicians' knowledge about this new approach.
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Affiliation(s)
- Mehmet Murat Seval
- Department of Obstetrics and Gynecology, Ankara University School of Medicine, Ankara, Türkiye
| | - Bulut Varlı
- Department of Obstetrics and Gynecology, Ankara University School of Medicine, Ankara, Türkiye
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A Case Study of Multiple Maintenance Efficacy in Gynaecological Surgery Assessed by Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8574000. [PMID: 35979051 PMCID: PMC9377963 DOI: 10.1155/2022/8574000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022]
Abstract
Deep learning is a new learning concept and a highly effective way of learning, which is still being explored in the field of nursing education. This paper analyses the effectiveness of interventions in perioperative gynaecological care using humanised care in the operating theatre and the impact of this model of care on patients’ psychological well-being and sleep quality. A deep learning-based vision robot was designed to provide higher quality of care for our human care and simplify our approach to gynaecological surgery. The anxiety and depression scores of the two groups were significantly improved after and before care, and the scores of the observation group were lower than those of the control group, with a statistically significant difference (
). The humanised care for gynaecological surgery patients in the perioperative period is more conducive to the improvement of their negative emotions and at the same time can improve the sleep quality of patients, so it can be further promoted.
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Riemma G, Vitale SG, Manchanda R, Rathore A, Török P, De Angelis C, Urman B, Sareri MI, La Verde M, Carugno J, De Franciscis P, Tesarik J. The role of hysteroscopy in reproductive surgery: today and tomorrow. J Gynecol Obstet Hum Reprod 2022; 51:102350. [DOI: 10.1016/j.jogoh.2022.102350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 01/16/2023]
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Delanerolle G, Yang X, Shetty S, Raymont V, Shetty A, Phiri P, Hapangama DK, Tempest N, Majumder K, Shi JQ. Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care. ACTA ACUST UNITED AC 2021; 17:17455065211018111. [PMID: 33990172 PMCID: PMC8127586 DOI: 10.1177/17455065211018111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, 'holistically' developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.
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Affiliation(s)
| | - Xuzhi Yang
- Southern University of Science and Technology, Shenzhen, China
| | | | | | - Ashish Shetty
- University College London, London, UK.,University College London NHS Foundation Trust, London, UK
| | - Peter Phiri
- Southern Health NHS Foundation Trust, Southampton, UK.,Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, UK
| | | | | | - Kingshuk Majumder
- University of Manchester Hospitals NHS Foundation Trust, Manchester, UK
| | - Jian Qing Shi
- Southern University of Science and Technology, Shenzhen, China.,The Alan Turing Institute, London, UK
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Ghoshal UC, Rai S, Kulkarni A, Gupta A. Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence. JGH OPEN 2020; 4:889-897. [PMID: 33102760 PMCID: PMC7578272 DOI: 10.1002/jgh3.12342] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/26/2020] [Accepted: 03/13/2020] [Indexed: 12/17/2022]
Abstract
Background and Aim About 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options. Methods Data of 263 patients (mean age 37.0 ± 14.0-years, 176, 77% male) with acute severe UC admitted during a 12-year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN). Results Of 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone (n = 14, 5.3%), intravenous (IV) hydrocortisone (n = 238, 90.5%), IV cyclosporine (n = 9, 3.4%), and inflixmab (n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C-reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower. Conclusion It is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors.
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Affiliation(s)
- Uday C Ghoshal
- Department of GastroenterologySanjay Gandhi Postgraduate Institute of Medical SciencesLucknowIndia
| | - Sushmita Rai
- Department of GastroenterologySanjay Gandhi Postgraduate Institute of Medical SciencesLucknowIndia
| | - Akshay Kulkarni
- Department of GastroenterologySanjay Gandhi Postgraduate Institute of Medical SciencesLucknowIndia
| | - Ankur Gupta
- Department of GastroenterologySanjay Gandhi Postgraduate Institute of Medical SciencesLucknowIndia
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Iftikhar P, Kuijpers MV, Khayyat A, Iftikhar A, DeGouvia De Sa M. Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice. Cureus 2020; 12:e7124. [PMID: 32257670 PMCID: PMC7105008 DOI: 10.7759/cureus.7124] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) is growing exponentially in various fields, including medicine. This paper reviews the pertinent aspects of AI in obstetrics and gynecology (OB/GYN) and how these can be applied to improve patient outcomes and reduce the healthcare costs and workload for clinicians. Herein, we will address current AI uses in OB/GYN, and the use of AI as a tool to interpret fetal heart rate (FHR) and cardiotocography (CTG) to aid in the detection of preterm labor, pregnancy complications, and review discrepancies in its interpretation between clinicians to reduce maternal and infant morbidity and mortality. AI systems can be used as tools to create algorithms identifying asymptomatic women with short cervical length who are at risk of preterm birth. Additionally, the benefits of using the vast data capacity of AI storage can assist in determining the risk factors for preterm labor using multiomics and extensive genomic data. In the field of gynecological surgery, the use of augmented reality helps surgeons detect vital structures, thus decreasing complications, reducing operative time, and helping surgeons in training to practice in a realistic setting. Using three-dimensional (3D) printers can provide materials that mimic real tissues and also helps trainees to practice on a realistic model. Furthermore, 3D imaging allows better depth perception than its two-dimensional (2D) counterpart, allowing the surgeon to create preoperative plans according to tissue depth and dimensions. Although AI has some limitations, this new technology can improve the prognosis and management of patients, reduce healthcare costs, and help OB/GYN practitioners to reduce their workload and increase their efficiency and accuracy by incorporating AI systems into their daily practice. AI has the potential to guide practitioners in decision-making, reaching a diagnosis, and improving case management. It can reduce healthcare costs by decreasing medical errors and providing more dependable predictions. AI systems can accurately provide information on the large array of patients in clinical settings, although more robust data is required.
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Affiliation(s)
| | - Marcela V Kuijpers
- Obstetrics and Gynecology, Universidad de Ciencias Medicas, San José, CRI
| | - Azadeh Khayyat
- Internal Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IRN
| | - Aqsa Iftikhar
- Bioinformatics, City College of New York, New York, USA
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Mont MA, Krebs VE, Backstein DJ, Browne JA, Mason JB, Taunton MJ, Callaghan JJ. Artificial Intelligence: Influencing Our Lives in Joint Arthroplasty. J Arthroplasty 2019; 34:2199-2200. [PMID: 31445865 DOI: 10.1016/j.arth.2019.08.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
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