MTA-SZTE Research Group on Artificial Intelligence
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A comparative study of femtosecond pulsed and continuous wave lasers on physiological responses through activation of phytochromes in seeds
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Kiégés, egészségi állapot önértékelése és pszichoszomatikus tünetek vizsgálata pályakezdő ápolók körében – összefüggésben a munkahelyi társas támogatással [Burnout, Self-Assessment of Health Status, and Psychosomatic Symptoms among Novice Nurses – in Relation to Social Support in the Workplace]
Get PDFDifferences in left ventricular functional properties in healthy adults with greater end-diastolic versus end-systolic aortic valve annular area—detailed analysis from the three-dimensional speckle-tracking echocardiographic MAGYAR-Healthy Study
Get PDFMode-Specific Quantum and Quasi-Classical Trajectory Dynamics of the F– + CH3I → I– + CH3F SN2 Reaction
No full textCore/shell BCN@Cu heterostructures via electroless deposition for interface-tailored electroactive materials
Get PDFCopper thin layer was deposited onto boron carbon nitride (BCN) by electroless plating and the fabricated composite was utilized in electrochemical nitrate reduction into ammonia
Machine Learning-Based Prediction of IVF Outcomes: The Central Role of Female Preprocedural Factors
Get PDFObjectives: We aimed to develop and validate a per-cycle prediction model for in vitro fertilization (IVF) success using only preprocedural clinical variables available at the first consultation. Methods: We retrospectively analysed 1243 IVF/ICSI cycles (University of Szeged, 21 January 2022–12 December 2023). An Extreme Gradient Boosting (XGBoost version 1.7.7.1) classifier was trained on 14 baseline predictors (e.g., female age, AMH, BMI, FSH, LH, sperm concentration/motility, and infertility duration). A parsimonious 9-variable model was derived by feature importance. Model performance was assessed on the untouched test set and, as a final step, on an independent same-centre external validation cohort (n = 92) without re-fitting or recalibration. Results: The 9-variable model achieved an AUC of 0.876 on the internal test set, with an accuracy of 81.70% (95% CI 76.30–86.30%), sensitivity of 75.60%, specificity of 84.40%, PPV of 68.60%, and NPV of 88.50%. In external validation, the model maintained strong performance with an accuracy of 78.30%, confirming consistent discrimination on an independent same-centre cohort. Female age was the dominant high-impact feature, while AMH and BMI acted as “workhorse” predictors, and male factors added incremental value. Conclusions: IVF outcome can be predicted at the first visit using routinely collected preprocedural data. The model showed consistent discrimination internally and in external validation, supporting its potential utility for early, individualized counselling and treatment planning