OBJECTIVE: We conducted a scoping review to identify the types of interventions targeting PFH-CRC, their effectiveness in increasing CRC screening uptake, and the elements associated with the outcomes.
METHODS: The Joanna Briggs Institute methodology for scoping review was followed. The search for eligible articles was conducted from the inception of each database until 17 July 2024 in PubMed, EMBASE, CINAHL, Cochrane, PsycINFO and Web of Science with no restrictions on language.
RESULTS: Thirty studies from 1995 to 2023 across 13 countries were included; mostly from high-income countries. There was considerable variability in study design, intervention characteristics, and screening outcomes. Eleven studies used theoretical frameworks in intervention development. Fourteen studies reported statistically significant increases in screening uptake among PFH-CRC, most using complex, multiple-component interventions. Tailored print materials and patient navigation more consistently demonstrated increased screening uptake, while counselling yielded mixed results.
CONCLUSION: Interventions for promoting CRC screening uptake in PFH-CRC commonly incorporate print material, patient navigation and counselling, often combined into complex interventions. Future research should include more implementation studies to translate these interventions into real-world settings. Additionally, there are gaps in research from low- and middle-income countries, highlighting the need for further research in these resource-limited settings.
RESULTS: The oocyte AI model demonstrated area under the curve (AUC) up to 0.65 on two blind test datasets. High sensitivity for predicting competent oocytes (83-88%) was offset by lower specificity (26-36%). Exclusion of confounding biological variables (male factor infertility and maternal age ≥35 years) improved AUC up to 14%, primarily due to increased specificity. AI score correlated with size of the zona pellucida and perivitelline space, and ooplasm appearance. AI score also correlated with blastocyst expansion grade and morphological quality. The sum of AI scores from oocytes in group culture images predicted the formation of two or more usable blastocysts (AUC 0.77).
CONCLUSION: An AI model to evaluate oocyte competence was developed using federated learning, representing an essential step in protecting patient data. The AI model was significantly predictive of oocyte competence, as defined by usable blastocyst formation, which is a critical factor for IVF success. Potential clinical utility ranges from selective oocyte fertilization to guiding treatment decisions regarding additional rounds of oocyte retrieval.
DESIGN: In total, 10,677 oocyte images with associated metadata were collected prospectively by eight IVF clinics across six countries. AI training used federated learning, where data were retained on regional servers to comply with data privacy laws. The final AI model required a single image as input to evaluate oocyte competence, which was defined by the formation of a usable blastocyst (≥expansion grade 3 by day 5 or 6 post ICSI).