ACEMS Members in AI Hub's Medical Datathon

ACEMS members contributed to the success of Queensland AI Hub's inaugural Medical Datathon, delivering presentations and teaming with others to apply diverse expertise to help solve problems in health.

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Owen Forbes

ACEMS masters student Owen Forbes' multi-disciplinary team realised success from their work, as joint People's Choice Award winners and finalists for their "Computer-aided diagnosis of lung nodules (CADxLN)" project.

The team combined data science and clinician expertise and experience with the aim of developing an AI tool for hospitals, to reduce both the under- and over-diagnosis of cancer.

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The team's workflow diagram

The AI solution they pitched would: systematically improve nodule detection, to reduce the risk of radiologists missing early signs of cancer; and perform multi-variable lung cancer risk estimation, allowing rational triage of follow-up. Uniquely, by linking to iEMR, this model will incorporate nodule and patient characteristics and produce an automated report integrated into the final CT report subject to radiologist endorsement.

"The datathon experience was a fun and invaluable learning opportunity and our team plans to continue working together to develop our proposed AI tool," says Owen.

Owen will receive support from the Queensland AI Hub and AWS, and may also apply to ACEMS funding schemes for research.

Owen recommends other students and researchers participate in similar hackathons/datathons to benefit from a multi-disciplinary team approach to solving a significant real-world problem in health or other domains of interest.