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  1. Article ; Online: The Thyroid Hormone Axis and Female Reproduction.

    Brown, Ethan D L / Obeng-Gyasi, Barnabas / Hall, Janet E / Shekhar, Skand

    International journal of molecular sciences

    2023  Volume 24, Issue 12

    Abstract: Thyroid function affects multiple sites of the female hypothalamic-pituitary gonadal (HPG) axis. Disruption of thyroid function has been linked to reproductive dysfunction in women and is associated with menstrual irregularity, infertility, poor ... ...

    Abstract Thyroid function affects multiple sites of the female hypothalamic-pituitary gonadal (HPG) axis. Disruption of thyroid function has been linked to reproductive dysfunction in women and is associated with menstrual irregularity, infertility, poor pregnancy outcomes, and gynecological conditions such as premature ovarian insufficiency and polycystic ovarian syndrome. Thus, the complex molecular interplay between hormones involved in thyroid and reproductive functions is further compounded by the association of certain common autoimmune states with disorders of the thyroid and the HPG axes. Furthermore, in prepartum and intrapartum states, even relatively minor disruptions have been shown to adversely impact maternal and fetal outcomes, with some differences of opinion in the management of these conditions. In this review, we provide readers with a foundational understanding of the physiology and pathophysiology of thyroid hormone interactions with the female HPG axis. We also share clinical insights into the management of thyroid dysfunction in reproductive-aged women.
    MeSH term(s) Pregnancy ; Female ; Humans ; Adult ; Reproduction/physiology ; Thyroid Hormones ; Thyroid Diseases ; Polycystic Ovary Syndrome/complications
    Chemical Substances Thyroid Hormones
    Language English
    Publishing date 2023-06-06
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms24129815
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Ethical Incorporation of Artificial Intelligence into Neurosurgery: A Generative Pre-Trained Transformer Chatbot-Based, Human-Modified Approach.

    Shlobin, Nathan A / Ward, Max / Shah, Harshal A / Brown, Ethan D L / Sciubba, Daniel M / Langer, David / D'Amico, Randy S

    World neurosurgery

    2024  

    Abstract: Introduction: Artificial intelligence (AI) has become increasingly used in neurosurgery. Generative pre-trained transformers (GPTs) have been of particular interest. However, ethical concerns regarding the incorporation of AI into the field remain ... ...

    Abstract Introduction: Artificial intelligence (AI) has become increasingly used in neurosurgery. Generative pre-trained transformers (GPTs) have been of particular interest. However, ethical concerns regarding the incorporation of AI into the field remain underexplored. We delineate key ethical considerations using a novel GPT-based, human-modified approach, synthesize the most common considerations, and present an ethical framework for the involvement of AI in neurosurgery.
    Methods: GPT-4, ChatGPT, Bing Chat / Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can artificial intelligence be ethically incorporated into neurosurgery?". Then, a layered GPT-based thematic analysis was performed. The authors synthesized the results into considerations for the ethical incorporation of AI into neurosurgery. Separate Pareto analyses with 20% threshold and 10% threshold were conducted to determine salient themes. The authors refined these salient themes.
    Results: Twelve key ethical considerations focusing on stakeholders, clinical implementation, and governance were identified. Refinement of the Pareto analysis of the top 20% most salient themes in the aggregated GPT outputs yielded ten key considerations. Additionally, from the top 10% most salient themes, five considerations were retrieved. An ethical framework for the use of AI in neurosurgery was developed.
    Conclusion: It is critical to address the ethical considerations associated with the use of AI in neurosurgery. The framework described in this manuscript may facilitate the integration of AI into neurosurgery, benefitting both patients and neurosurgeons alike. We urge neurosurgeons to use AI only for validated purposes and caution against automatic adoption of its outputs without neurosurgeon interpretation.
    Language English
    Publishing date 2024-05-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2534351-8
    ISSN 1878-8769 ; 1878-8750
    ISSN (online) 1878-8769
    ISSN 1878-8750
    DOI 10.1016/j.wneu.2024.04.165
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1159: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article: Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.

    Barrera, Francisco J / Brown, Ethan D L / Rojo, Amanda / Obeso, Javier / Plata, Hiram / Lincango, Eddy P / Terry, Nancy / Rodríguez-Gutiérrez, René / Hall, Janet E / Shekhar, Skand

    Frontiers in endocrinology

    2023  Volume 14, Page(s) 1106625

    Abstract: Introduction: Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in ... ...

    Abstract Introduction: Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.
    Methods: We applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.
    Results: 135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).
    Conclusion: Artificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.
    Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.
    MeSH term(s) Female ; Humans ; Artificial Intelligence ; Polycystic Ovary Syndrome/diagnosis ; Proteomics ; Machine Learning ; Cluster Analysis
    Language English
    Publishing date 2023-09-18
    Publishing country Switzerland
    Document type Systematic Review ; Research Support, N.I.H., Intramural
    ZDB-ID 2592084-4
    ISSN 1664-2392
    ISSN 1664-2392
    DOI 10.3389/fendo.2023.1106625
    Database MEDical Literature Analysis and Retrieval System OnLINE

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