RESUMEN
Various studies have emphasized the importance of identifying the optimal Trigger Timing (TT) for the trigger shot in In Vitro Fertilization (IVF), which is crucial for the successful maturation and release of oocytes, especially in minimal ovarian stimulation treatments. Despite its significance for the ultimate success of IVF, determining the precise TT remains a complex challenge for physicians due to the involvement of multiple variables. This study aims to enhance TT by developing a machine learning multi-output model that predicts the expected number of retrieved oocytes, mature oocytes (MII), fertilized oocytes (2 PN), and useable blastocysts within a 48-h window after the trigger shot in minimal stimulation cycles. By utilizing this model, physicians can identify patients with possible early, late, or on-time trigger shots. The study found that approximately 27 % of treatments administered the trigger shot on a suboptimal day, but optimizing the TT using the developed Artificial Intelligence (AI) model can potentially increase useable blastocyst production by 46 %. These findings highlight the potential of predictive models as a supplementary tool for optimizing trigger shot timing and improving IVF outcomes, particularly in minimal ovarian stimulation. The experimental results underwent statistical validation, demonstrating the accuracy and performance of the model. Overall, this study emphasizes the value of AI prediction models in enhancing TT and making the IVF process safer and more efficient.
Asunto(s)
Fertilización In Vitro , Aprendizaje Automático , Inducción de la Ovulación , Humanos , Femenino , Inducción de la Ovulación/métodos , Fertilización In Vitro/métodos , AdultoRESUMEN
INTRODUCTION: Guidelines have recommended prophylactic cranial irradiation (PCI) for patients with limited-stage small-cell lung cancer with at least a partial response after thoracic chemoradiation. However, the survival advantage has been small and was observed in an era before magnetic resonance imaging and surveillance. Neurotoxicity also remains a concern, especially in older adults. Thus, patients have a complex value-laden decision to make. We sought to better understand the role physicians play in patient decision making and introduce a patient decision aid (PDA) to potentially facilitate these discussions. MATERIALS AND METHODS: An e-mail survey was sent to International Association for the Study of Lung Cancer members querying their personal perspectives and professional recommendations regarding PCI for limited-stage small-cell lung cancer. RESULTS: We received 295 responses. Most were from the United States (35%) and Europe (35%) and were radiation (45%) or medical (43%) oncologists. Of those responding, 88% and 50% reported they would recommend PCI to a 50- and 70-year-old patient, respectively. Also, 79% reported that they would wish to receive PCI if faced with this decision. The physicians who would have chosen PCI if faced with the decision were 27.6 and 12.9 times more likely to recommend PCI to a 50- and 70-year-old patient, respectively, than were physicians who would not undergo PCI themselves. Most of the respondents had positive responses to the proposed PDA. CONCLUSION: Physician bias appears to play a role in PCI counseling, and most physicians reported that the provided PDA was better than their present method for discussing PCI and would help patients make such value-laden choices.