CARNAX – Computer Assisted Reporting of Neonatal Abdominal X-rays
AI-based medical imaging tool for detection of intestinal perforation.
Modality:
Abdominal and Chest X-Rays (Paediatric)
Pathology:
Perforation of the intestine in preterm new-born babies
Status:
Developing
CSC Lead: Simone
About one in every ten newborn babies requires support on a neonatal unit. Most of these infants are premature (born at less than 37 weeks of pregnancy). Premature infants are at greater risk of intestinal problems which can lead to infection, inflammation or a hole in the wall of the bowel. Clinical teams that look after these babies rely on x-rays of the abdomen to know if there is a problem with a child’s gut. On occasion problems are missed because the x-ray is not looked at carefully enough or because the clinician is tired or inexperienced. A computer algorithm can be trained to recognise abnormalities on an x-ray. This system can then be used to alert the clinical team early if there is a problem. This could prompt earlier treatment and prevent babies from getting sicker.
Other considerations Perforation is not straightforward to detect, it might be missed by a junior radiologist or if the radiologist is tired
Clinical lead: Hammad Khan
Project Plan
1. Meeting of all persons involved to determine AI specifications. 4. Model training
2. Setting technical and system requirements for AI model.
3. Dataset curation (retrospective).
5. Model testing
6. Implementation
7. Audit
References
Kwon et al 2020