A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

被引:15
作者
Malani, Sagar N. [1 ]
Shrivastava, Deepti [1 ]
Raka, Mayur S. [1 ]
机构
[1] Datta Meghe Inst Higher Educ & Res, Jawaharlal Nehru Med Coll, Dept Obstet & Gynecol, Wardha, India
关键词
ultrasonography; postpartum period; artificial neural networks; gynecology; obstetrics; artificial intelligence in medicine; CANCER;
D O I
10.7759/cureus.34891
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
引用
收藏
页数:10
相关论文
共 38 条
  • [11] Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum
    Goodday, S. M.
    Karlin, E.
    Brooks, A.
    Chapman, C.
    Karlin, D. R.
    Foschini, L.
    Kipping, E.
    Wildman, M.
    Francis, M.
    Greenman, H.
    Li, Li
    Schadt, E.
    Ghassemi, M.
    Goldenberg, A.
    Cormack, F.
    Taptiklis, N.
    Centen, C.
    Smith, S.
    Friend, S.
    [J]. NPJ DIGITAL MEDICINE, 2022, 5 (01)
  • [12] Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy
    Gupta, Krishan
    Balyan, Kirti
    Lamba, Bhumika
    Puri, Manju
    Sengupta, Debarka
    Kumar, Manisha
    [J]. JOURNAL OF MATERNAL-FETAL & NEONATAL MEDICINE, 2022, 35 (25) : 5587 - 5594
  • [13] Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice
    Iftikhar, Pulwasha
    Kuijpers, Marcela, V
    Khayyat, Azadeh
    Iftikhar, Aqsa
    De Sa, Maribel DeGouvia
    [J]. CUREUS JOURNAL OF MEDICAL SCIENCE, 2020, 12 (02)
  • [14] Artificial intelligence in medical ultrasonography: driving on an unpaved road
    Kim, Young H.
    [J]. ULTRASONOGRAPHY, 2021, 40 (03) : 313 - 317
  • [15] Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations
    Lin, M.
    He, X.
    Guo, H.
    He, M.
    Zhang, L.
    Xian, J.
    Lei, T.
    Xu, Q.
    Zheng, J.
    Feng, J.
    Hao, C.
    Yang, Y.
    Wang, N.
    Xie, H.
    [J]. ULTRASOUND IN OBSTETRICS & GYNECOLOGY, 2022, 59 (03) : 304 - 316
  • [16] Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor
    Liu, Lijue
    Jiao, Yongxia
    Li, Xihong
    Ouyang, Yan
    Shi, Danni
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2020, 196
  • [17] Medical error-the third leading cause of death in the US
    Makary, Martin A.
    Daniel, Michael
    [J]. BMJ-BRITISH MEDICAL JOURNAL, 2016, 353
  • [18] A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters
    Malek, Mahrooz
    Gity, Masoumeh
    Alidoosti, Azadeh
    Oghabian, Zeinab
    Rahimifar, Pariya
    Ebrahimi, Seyede Mandieh Seyed
    Tabibian, Elnaz
    Oghabian, Mohammad Ali
    [J]. EUROPEAN JOURNAL OF RADIOLOGY, 2019, 110 : 203 - 211
  • [19] The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women
    Pergialiotis, V
    Pouliakis, A.
    Parthenis, C.
    Damaskou, V
    Chrelias, C.
    Papantoniou, N.
    Panayiotides, I
    [J]. PUBLIC HEALTH, 2018, 164 : 1 - 6
  • [20] Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening
    Sakai, Akira
    Komatsu, Masaaki
    Komatsu, Reina
    Matsuoka, Ryu
    Yasutomi, Suguru
    Dozen, Ai
    Shozu, Kanto
    Arakaki, Tatsuya
    Machino, Hidenori
    Asada, Ken
    Kaneko, Syuzo
    Sekizawa, Akihiko
    Hamamoto, Ryuji
    [J]. BIOMEDICINES, 2022, 10 (03)