View source for Alder Hey Children's Hospital

Contact: Megan Foden <Megan.Foden@alderhey.nhs.uk>

Speech Error Detection & Correction
Children with cleft palate often experience specific speech errors that require targeted feedback to correct. Traditional speech therapy sessions are time-consuming and may not provide immediate, specific guidance during at-home practice. This project aims to develop an interactive tool that detects cleft-related speech errors and offers real-time, personalised feedback. The tool must create a baseline and then learn as the child practices how they are improving.  The goal is to support speech therapists by providing patients with an engaging way to practice and improve their speech outside of clinical sessions, enhancing the overall effectiveness of treatment.

Conversational Patient History for ASD and ADHD
Waiting lists for Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) assessments are growing, partly due to the extensive time specialists spend on developmental history taking-often hours per patient. This project aims to create a conversational AI tool that allows children and their parents to provide comprehensive patient history in a natural, dialogue-based manner. By engaging users in a conversational interface rather than requiring them to fill out lengthy forms, the system can extract necessary clinical information and automatically format it into a report. This innovation seeks to streamline the initial assessment process, saving specialists' time and potentially reducing waiting lists.

Generative Custom Heart Models
Communicating complex heart conditions to patients and their families is challenging. Clinicians often resort to hand-drawn diagrams to explain cardiac anatomy, but these are non-standardized, vary in quality, and can be easily lost-leaving parents without a clear understanding. Additionally, patients with multiple defects find that standard diagrams do not accurately represent their unique conditions. This project aims to develop a generative AI tool that allows clinicians to create custom heart models through voice (or other, more applicable) interactions. By simply describing the anatomical issues, the AI will generate accurate, personalised animations illustrating the patient's specific condition. This tool will standardize visual explanations, make information easier to digest for families, enhancing overall understanding and care.