4 active · 1 completed

Our Research

As AIM, we are driven by the potential of AI and medicine converging to revolutionise healthcare, striving to transform lives and create a healthier future for all.

Explore Projects
Featured Research
RSCH-00In Progress
Retinal Imaging

GAVE

Generalised Analysis of Vessels in Eye.

SegmentationFFARetinal ImagingBiomarkers

Distinguishing arteries from veins in the back of the eye and measuring each accurately — from standard colour photographs to dye-enhanced fluorescein angiography (FFA) — across artery/vein segmentation, cross-modal FFA-guided segmentation, and vascular biomarker quantification.

Why it matters

Accurate vessel measurements — widths, arteriovenous ratio, vessel density, and branching complexity — give doctors clinically meaningful biomarkers for diagnosing both eye and whole-body conditions.

FieldRetinal Imaging
StatusIn Progress
Year2026
TeamIT Faculty + Medical Faculty
Live Research

Current Projects

Every project pairs students from IT and medical faculties to turn machine learning into real clinical impact.

3 in progress
RSCH-01In Progress
Multimodal AI

CHIMERA Agent

Combining histology, medical imaging and molecular data for prognosis and diagnosis.

A clinical prostate-cancer decision-making agent that analyses multiparametric MRI reports to estimate the probability of clinically significant cancer, recommends whether a biopsy is warranted, and provides structured reasoning for how the evidence was gathered and resolved.

Why it matters

Prostate cancer is the 4th most common cancer worldwide, yet current pathways drive overdiagnosis and invasive biopsies. Transparent AI decision support could reduce unnecessary, risky procedures.

FieldMultimodal AI
StatusIn Progress
Year2026
LLMMRIMultimodalClinical Decision
IT Faculty + Medical Faculty
RSCH-02In Progress
Radiology

TREAT-MMTB

Efficient AI technologies for multimodal management of tuberculosis.

Deep-learning models that read chest X-rays alongside clinical metadata to detect and segment cavitary lesions and predict the Timika severity score, generalising across patient populations in Korea, Mongolia, Peru, and the Philippines.

Why it matters

A shortage of trained radiologists makes TB screening slow and subjective. In the regions where TB claims the most lives, AI can widen access to fast, consistent diagnosis and treatment.

FieldRadiology
StatusIn Progress
Year2026
SegmentationChest X-RayMultimodalRadiology
IT Faculty + Medical Faculty
RSCH-03In Progress
Clinical NLP

COHORT-X

Extracting executable cohort definitions for medical imaging research.

NLP systems that turn messy free-text cohort selection criteria from biomedical literature into structured, computable representations — extracting inclusion and exclusion criteria as triples and resolving medical conditions to ICD-10-CM codes.

Why it matters

There is no benchmark structure for cohort selection criteria, making imaging studies hard to reproduce. Standardised, computable criteria improve reproducibility and fairness in future research.

FieldClinical NLP
StatusIn Progress
Year2026
NLPText ProcessingICD-10-CMReproducibility
IT Faculty + Medical Faculty
Archive · Completed
RSCH-04Completed
Neuroimaging

Ischemic Stroke Detection

Detecting stroke in brain scans with machine learning.

A machine-learning model that identifies blocked blood vessels and damaged tissue in brain scans to detect whether an ischemic stroke has occurred, built on the ISLES22 dataset using HighResNet.

Why it matters

An ischemic stroke occurs when a vessel supplying blood to the brain is blocked. Fast, automated detection supports earlier intervention when every minute counts.

FieldNeuroimaging
StatusCompleted
Year2024
HighResNetISLES22Brain MRISegmentation
IT Faculty + Medical Faculty

Want to build with us?

We're looking for students fluent in Python and curious about machine learning, neural networks, and the medicine they could transform.

Join the TeamAbout AIM