Personalizing the first dose of FSH for IVF/ICSI patients through machine learning: a non-inferiority study protocol for a multi-center randomized controlled trial.

BACKGROUND: Adequately selecting the initial follicle-stimulating hormone (FSH) dose during controlled ovarian stimulation (COS) is key for success in assisted reproduction. The objective of COS is to obtain an optimal number of oocytes to increase the chances of achieving a pregnancy, while avoiding complications for the patient. Current clinical protocols do achieve good results for the majority of patients, but further refinements in individualized FSH dosing may reduce the risk of poor ovarian response while also limiting the risk of ovarian hyperstimulation syndrome (OHSS) risk. Models to select the first FSH dose in COS have been presented in literature with promising results. However, most have only been developed and tested in normo-ovulatory women under the age of 40 years. METHODS: This is a randomized, controlled, multicenter, single blinded, clinical trial. This study will be performed in 236 first cycle in vitro fertilization (IVF) and/or ICSI (intracytoplasmic sperm injection) patients, randomized 1:1 in two arms. In the intervention arm, the dose of FSH will be assigned by a machine learning (ML) model called IDoser, while in the control arm, the dose will be determined by the clinician following standard practice. Stratified block randomization will be carried out depending on the patient being classified as expected low responder, high responder, or normo-responder. Patients will complete their participation in the trial once the first embryo transfer result is known. The primary outcome of the study is the number of metaphase II (MII) oocytes retrieved at ovarian pick up (OPU) and the hypothesis of non-inferiority of the intervention arm compared to the control. Secondary outcomes include the number of cycle cancelations (due to low response or no retrieval of mature oocytes), risk of ovarian hyperstimulation syndrome (OHSS), and clinical pregnancy and live birth rates per first transfer. DISCUSSION: To our knowledge, this is the first randomized trial to test clinical performance of an all-patient inclusive model to select the first dose of FSH for COS. Prospective trials for machine learning (ML) models in healthcare are scarce but necessary for clinical application. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05948293 . Registered on 14 July 2023.

Manual versus deep learning measurements to evaluate cumulus expansion of bovine oocytes and its relationship with embryo development in vitro.

Cumulus expansion is an important indicator of oocyte maturation and has been suggested to be indicative of greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. In this manuscript, the reliability of three cumulus expansion measurement methods was assessed, and a deep learning model was created to automatically perform the measurement. Cumulus expansion of 232 cumulus-oocyte complexes was evaluated by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. The reliability of the methods was calculated in terms of intraclass-correlation coefficients (ICC) for both inter- and intra-observer agreements. The area method resulted in the best overall inter-observer agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring methods, respectively. Therefore, the area method served as the base to create a deep learning model, AI-xpansion, which reaches a human-level performance in terms of average rank, bias and variance. To evaluate the accuracy of the methods, the results of cumulus expansion calculations were linked to embryonic development. Cumulus expansion had increased significantly in oocytes that achieved successful embryo development when measured by AI-xpansion, the area- or 3-distance method, while this was not the case for the scoring method. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level precision and high accuracy and could therefore be a valuable prospective tool for embryologists.

Supporting first FSH dosage for ovarian stimulation with machine learning.

RESEARCH QUESTION: Is it possible to identify accurately the optimal first dose of FSH in ovarian stimulation by means of a machine learning model? DESIGN: Observational study (2011-2021) including first IVF cycles with own oocytes. A total of 2713 patients from five private reproductive centres were included in the development phase (2011-2019) and 774 in the validation phase (2020-2021). Predictor variables included age, BMI, AMH, AFC and previous live births. Performance was measured with a proposed score based on the number of MII oocytes retrieved and dose received, recommended, or both. RESULTS: The included cycles were from women aged 37.7 ± 4.4 years (18-45 years), with a BMI of 23.5 ± 4.2 kg/m2, AMH of 2.4 ± 2.3 ng/ml, AFC of 11.3 ± 7.6, and an average number of MII obtained 6.9 ± 5.4. The model reached a mean performance score of 0.87 (95% CI 0.86 to 0.88) in the development phase, significantly better than for doses prescribed by clinicians for the same patients (0.83, 95% CI 0.82 to 0.84; P = 2.44 e-10). Mean performance score of the model recommendations was 0.89 (95% CI 0.88 to 0.90) in the validation phase, also significantly better than clinicians (0.84, 95% CI 0.82 to 0.86; P = 3.81 e-05). The model was shown to surpass the performance of standard practice. CONCLUSION: This machine learning model could be used as a training and learning tool for new clinicians, and as quality control for experienced clinicians.

Designing an app for home-based enriched Music-supported Therapy in the rehabilitation of patients with chronic stroke: a pilot feasibility study.

OBJECTIVE: After completing formal stroke rehabilitation programs, most patients do not achieve full upper limb motor function recovery. Music-supported Therapy (MST) can improve motor functionality post stroke through musical training. We designed a home-based enriched Music-supported Therapy (eMST) program to provide patients with chronic stroke the opportunity of continuing rehabilitation by themselves. We developed an app to conduct the eMST sessions at home with a MIDI-piano and percussion instruments. Here, we tested the feasibility of the eMST intervention using the novel app. METHOD: This is a pilot study where five patients with chronic stroke underwent a 10-week intervention of 3 sessions per week. Patients answered feasibility questionnaires throughout the intervention to modify aspects of the rehabilitation program and the app according to their feedback. Upper limb motor functions were evaluated pre- and post-intervention as well as speed and force tapping during daily piano performance. RESULTS: Patients clinically improved in upper limb motor function achieving the Minimal Detectable Change (MDC) or Minimal Clinically Important Difference (MCID) in most of motor tests. The app received high usability ratings post-intervention. CONCLUSION: The eMST program is a feasible intervention for patients with chronic stroke and its efficacy should be assessed in a clinical trial.

Enriched Music-supported Therapy for chronic stroke patients: a study protocol of a randomised controlled trial.

BACKGROUND: Residual motor deficits of the upper limb in patients with chronic stroke are common and have a negative impact on autonomy, participation and quality of life. Music-Supported Therapy (MST) is an effective intervention to enhance motor and cognitive function, emotional well-being and quality of life in chronic stroke patients. We have adapted the original MST training protocol to a home-based intervention, which incorporates increased training intensity and variability, group sessions, and optimisation of learning to promote autonomy and motivation. METHODS: A randomised controlled trial will be conducted to test the effectiveness of this enriched MST (eMST) protocol in improving motor functions, cognition, emotional well-being and quality of life of chronic stroke patients when compared to a program of home-based exercises utilizing the Graded Repetitive Arm Supplementary Program (GRASP). Sixty stroke patients will be recruited and randomly allocated to an eMST group (n = 30) or a control GRASP intervention group (n = 30). Patients will be evaluated before and after a 10-week intervention, as well as at 3-month follow-up. The primary outcome of the study is the functionality of the paretic upper limb measured with the Action Research Arm Test. Secondary outcomes include other motor and cognitive functions, emotional well-being and quality of life measures as well as self-regulation and self-efficacy outcomes. DISCUSSION: We hypothesize that patients treated with eMST will show larger improvements in their motor and cognitive functions, emotional well-being and quality of life than patients treated with a home-based GRASP intervention. TRIAL REGISTRATION: The trial has been registered at ClinicalTrials.gov and identified as NCT04507542 on 8 August 2020.

Note onset deviations as musical piece signatures.

A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields.