Development of a Dynamic Diagnosis Grading System for Infertility Using Machine Learning

Liao, Shujie; Pan, Wei; Dai, Wan-Qiang; Jin, Lei; Huang, Guocheng; Wang, Renjie; Hu, Cheng; Pan, Wei; Tu, Haiting

doi:10.1001/jamanetworkopen.2020.23654

Cited by 14 publications

(10 citation statements)

References 29 publications

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“…In their study, over 60,000 infertile couples' medical records were evaluated using a grading system that classified patients into 5 grades ranging from A to E. The worst grade, E, represented a 0.90% pregnancy rate, while the pregnancy rate in the A grade was 53.8%. The cross-validation results showed that the stability of the system was 95.9% (Liao et al, 2020). Letterie et al, evaluated a computer decision support system for day-to-day management of ovarian stimulation during IVF following key decisions made during an IVF cycle: [1] stop stimulation or continue stimulation.…”

Section: Ovarian Stimulation Managementmentioning

confidence: 99%

Artificial intelligence in the embryology laboratory: a review

Dimitriadis

Zaninović

Badiola

et al. 2022

Reproductive BioMedicine Online

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Section: Ovarian Stimulation Managementmentioning

confidence: 99%

Artificial intelligence in the embryology laboratory: a review

Dimitriadis

Zaninović

Badiola

et al. 2022

Reproductive BioMedicine Online

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“…Andrology and embryology labs can predict gamete viability, uterine health, and potential for intrauterine insemination (IUI) treatment using patientspecific models trained on electronic health records prior to recommending IVF treatment [24,25]. A recent prognostic study by Liao et al [26] consisting of 60,648 medical records of couples with infertility used a random forest model to automatically identify indicators associated with better pregnancy outcomes. Lower age and follicle-stimulating hormone level, higher antral follicle count, and greater endometrial thickness all contributed to a better outcome with compound effects, highlighting the need for considering multiple indicators in risk assessments.…”

Section: Infertility Diagnosismentioning

confidence: 99%

Current trends in artificial intelligence in reproductive endocrinology

Bhaskar

Chang

Wang

2022

Current Opinion in Obstetrics &Amp; Gynecology

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Purpose of reviewArtificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings. Recent findingsArtificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance.

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“…Machine learning can fully account for the interactions between characteristics and incorporate new data to update models, in contrast to traditional statistical analysis approaches, which rely on a preset equation ( 14). In the realm of assisted reproduction, machine learning methods have previously been applied to evaluate and predict pregnancy rates (15)(16)(17). Researchers also have attempted to construct regression models to assess ovarian reserve by integrating single biochemical and ultrasound markers (18)(19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

Assessment and quantification of ovarian reserve on the basis of machine learning models

Ding

Ren²,

Han³

et al. 2023

Front. Endocrinol.

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BackgroundEarly detection of ovarian aging is of huge importance, although no ideal marker or acknowledged evaluation system exists. The purpose of this study was to develop a better prediction model to assess and quantify ovarian reserve using machine learning methods.MethodsThis is a multicenter, nationwide population-based study including a total of 1,020 healthy women. For these healthy women, their ovarian reserve was quantified in the form of ovarian age, which was assumed equal to their chronological age, and least absolute shrinkage and selection operator (LASSO) regression was used to select features to construct models. Seven machine learning methods, namely artificial neural network (ANN), support vector machine (SVM), generalized linear model (GLM), K-nearest neighbors regression (KNN), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to construct prediction models separately. Pearson’s correlation coefficient (PCC), mean absolute error (MAE), and mean squared error (MSE) were used to compare the efficiency and stability of these models.ResultsAnti-Müllerian hormone (AMH) and antral follicle count (AFC) were detected to have the highest absolute PCC values of 0.45 and 0.43 with age and held similar age distribution curves. The LightGBM model was thought to be the most suitable model for ovarian age after ranking analysis, combining PCC, MAE, and MSE values. The LightGBM model obtained PCC values of 0.82, 0.56, and 0.70 for the training set, the test set, and the entire dataset, respectively. The LightGBM method still held the lowest MAE and cross-validated MSE values. Further, in two different age groups (20–35 and >35 years), the LightGBM model also obtained the lowest MAE value of 2.88 for women between the ages of 20 and 35 years and the second lowest MAE value of 5.12 for women over the age of 35 years.ConclusionMachine learning methods combining multi-features were reliable in assessing and quantifying ovarian reserve, and the LightGBM method turned out to be the approach with the best result, especially in the child-bearing age group of 20 to 35 years.

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Development of a Dynamic Diagnosis Grading System for Infertility Using Machine Learning

Cited by 14 publications

References 29 publications

Artificial intelligence in the embryology laboratory: a review

Artificial intelligence in the embryology laboratory: a review

Current trends in artificial intelligence in reproductive endocrinology

Assessment and quantification of ovarian reserve on the basis of machine learning models

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