Data-Driven Medicine in the Diagnosis and Treatment of Infertility
Abstract
:1. Introduction
- A conceptual shift from a disease-centric to a health-centric model: How infertility care is moving beyond a disease-based reactive model to a pro-active model focused on enhancing patients’ health and well-being.
- Better prevention, diagnosis and treatments: Sophisticated big data analysis of cohorts have allowed for the development of better strategies for diagnosis and treatments.
- ML/AI in ART Treatments: How ML/AI are currently being used to improve IVF across almost all stages of the treatment process.
- The participatory citizen: How the individual is empowered to drive their own reproductive health and well-being [16].
2. A Conceptual Shift from a Disease-Centric to a Health-Centric Model
3. The Era of Big Data Is Enabling Better Prevention, Diagnosis and Treatments
3.1. Biomarkers and Screening Tests Can Guide Treatment Decisions
3.2. Mechanistic Understanding of Disease Can Help Stratify Patients for Treatment
3.3. Integrative Modelling of Non-Genetic Exposures Could Help Infertility Prevention Strategies
3.4. Genetic Data Can Be Used to Define Optimal Controlled Ovarian Hyperstimulation (COH) Dosing Regimens
3.5. The Microbiome as an Important Emerging Health Data Stream in Infertility
4. Machine Learning Is Aiding ART Treatments
5. The Participatory Citizen: From a Disease-Centric Model to Active Wellness
6. Challenges to the Use Machine Learning and Big Data in the Infertility Sector
6.1. High Quality and Quantities of Data
6.2. Generalizability of Learning
6.2.1. Data Biases Introduced by Population Heterogeneity
6.2.2. Non-Stationarity in Treatment Data and Historical Biases
6.3. Algorithm Validation Using Double-Blinded Datasets
6.4. The Challenge of Translation to Clinical Practice
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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de Santiago, I.; Polanski, L. Data-Driven Medicine in the Diagnosis and Treatment of Infertility. J. Clin. Med. 2022, 11, 6426. https://doi.org/10.3390/jcm11216426
de Santiago I, Polanski L. Data-Driven Medicine in the Diagnosis and Treatment of Infertility. Journal of Clinical Medicine. 2022; 11(21):6426. https://doi.org/10.3390/jcm11216426
Chicago/Turabian Stylede Santiago, Ines, and Lukasz Polanski. 2022. "Data-Driven Medicine in the Diagnosis and Treatment of Infertility" Journal of Clinical Medicine 11, no. 21: 6426. https://doi.org/10.3390/jcm11216426