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Shahmoradi L, Azadbakht L, Farzi J, Kalhori SRN, Yazdipour AB, Solat F. Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review. BMC Womens Health 2024; 24:234. [PMID: 38610020 PMCID: PMC11015675 DOI: 10.1186/s12905-024-03074-3] [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: 01/12/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations. MATERIALS AND METHODS This study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality. RESULTS Most studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size. CONCLUSION Recommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research.
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Affiliation(s)
- Leila Shahmoradi
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Leila Azadbakht
- Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
- Endocrinology and Metabolism Research Centre, Tehran University of Medical Sciences, Tehran, Iran
| | - Jebraeil Farzi
- Health Information Technology Department, School of Allied Medical Sciences, Zabol University of Medical Sciences, Zabol, Balouchestan, Sistan, Iran
| | - Sharareh Rostam Niakan Kalhori
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Banaye Yazdipour
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
- Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran.
- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Fahimeh Solat
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
- Student Research Committee, Saveh University of Medical Sciences, Saveh, Iran.
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S NLC, S N. Analysis of risk factors in diabetics resulted from polycystic ovary syndrome in women by EDA analysis and machine learning techniques. Comput Methods Biomech Biomed Engin 2024; 27:77-97. [PMID: 37664890 DOI: 10.1080/10255842.2023.2252957] [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: 03/10/2023] [Revised: 07/25/2023] [Accepted: 08/17/2023] [Indexed: 09/05/2023]
Abstract
This study discusses the relationship between Polycystic Ovary Syndrome (PCOS) and diabetes in women, which has become increasingly prevalent due to changing lifestyles and environmental factors. The characteristic that distinguishes women with PCOS is hyperandrogenism which results from abnormal ovarian or adrenal function, which leads to the overproduction of androgens. Excessive androgens in women increase the risk of Type 2 diabetes (T2D) and insulin resistance (IR). Nowadays, diabetes affects people of all ages and is linked to factors such as lifestyle, genetics, stress, and aging. Diabetes, the uncontrolled high blood sugar level can potentially harm kidneys, nerves, eyes, and other organs and there is no cure, making it a concerning disease in developing nations. This research tried to submit the evidence through feature-wise correlation analyses between PCOS and diabetes. Hence, this model utilized the Exploratory Data Analysis (EDA) and the Elbow clustering algorithms for the experimental purpose in which the EDA deeply analyzed the features of PCOS and diabetes and recorded a positive correlation of 95%. The Elbow clustering technique is employed for verifying the correlations identified through EDA. Although limited research exists on this specific disease, this work provides potential evidence for the research community by evaluating the clustering results using Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index.
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Affiliation(s)
- Nancy Lima Christy S
- Full-Time Research Scholar, K.S.R College of Engineering, Erode, Tamilnadu, India
| | - Nithyakalyani S
- Professor/Information Technology, K.S.R College of Engineering, Erode, Tamilnadu, India
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Guixue G, Yifu P, Yuan G, Xialei L, Fan S, Qian S, Jinjin X, Linna Z, Xiaozuo Z, Wen F, Wen Y. Progress of the application clinical prediction model in polycystic ovary syndrome. J Ovarian Res 2023; 16:230. [PMID: 38007488 PMCID: PMC10675861 DOI: 10.1186/s13048-023-01310-2] [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: 07/02/2023] [Accepted: 11/05/2023] [Indexed: 11/27/2023] Open
Abstract
Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.
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Affiliation(s)
- Guan Guixue
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Pu Yifu
- Laboratory of Genetic Disease and Perinatal Medicine, Key laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Gao Yuan
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Liu Xialei
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Shi Fan
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Sun Qian
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Xu Jinjin
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Zhang Linna
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Zhang Xiaozuo
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Feng Wen
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Yang Wen
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China.
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China.
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China.
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [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: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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Lai W, Shen N, Zhu H, He S, Yang X, Lai Q, Li R, Ji S, Chen L. Identifying risk factors for polycystic ovary syndrome in women with epilepsy: A comprehensive analysis of 248 patients. J Neuroendocrinol 2023; 35:e13250. [PMID: 36942563 DOI: 10.1111/jne.13250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/04/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023]
Abstract
To assess the risk factors for polycystic ovary syndrome (PCOS) in women with epilepsy (WWE) and develop a practical approach for PCOS screening based on clinical characteristic, blood indicator, and anti-seizure medication (ASM) profiles. This cross-sectional study was conducted with 248 WWE who were consecutively enrolled from the Epilepsy Center of West China Hospital between April 2021 and March 2022. The epilepsy characteristics, blood indicators, and use of ASMs were compared between WWE with and without PCOS. Multivariate logistic regression was used to identify the factors independently associated with PCOS. The differential analysis showed that younger age at onset of epilepsy (<13 years), a history of birth hypoxia, obesity (BMI ≥25 kg/m2 ), use of levetiracetam (LEV) (≥1 year), higher levels of cholesterol, luteinizing hormone (LH) and anti-Müllerian hormone (AMH), and lower levels of sex hormone-binding globulin were associated with PCOS (p < .05). Multivariate logistic regression identified that obesity (BMI ≥25 kg/m2 ), use of LEV (≥1 year), and higher levels of AMH and LH were independently associated with PCOS in WWE (p < .05). Obesity (BMI ≥25 kg/m2 ), LEV use (≥1 year), and elevated AMH and LH levels suggest an increased in the probability of occurrence of PCOS in WWE. The combination of these profiles provides a practical approach for screening PCOS in WWE.
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Affiliation(s)
- Wanlin Lai
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Ning Shen
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Huili Zhu
- Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Chengdu, China
| | - Shixu He
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Ximeng Yang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Qi Lai
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Rui Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Shuming Ji
- Office of Programme Design and Statistics, Clinical Research Management Department, West China Hospital of Sichuan University, Chengdu, China
| | - Lei Chen
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
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