The role of AI in imaging follow-up for aspergillosis

Aspergillosis is not a single disease, but a spectrum of related conditions, including allergic bronchopulmonary aspergillosis (ABPA), chronic pulmonary aspergillosis (CPA), aspergilloma, subacute invasive aspergillosis, and Aspergillus bronchitis. Across this spectrum, imaging plays a central role in assessment, monitoring, and treatment planning.

Artificial intelligence (AI) is increasingly being explored in chest imaging as a supportive tool, particularly in the follow-up of chronic and complex lung disease. Its potential value in aspergillosis lies not in making diagnoses, but in consistent, data-driven image comparison.

Why aspergillosis is challenging for imaging interpretation

Imaging findings in aspergillosis are often:

• Non-specific, overlapping with tuberculosis, non-tuberculous mycobacterial lung disease, lung cancer, vasculitis, and post-infectious scarring

• Heterogeneous, even within the same patient

• Slowly evolving, making change difficult to judge by eye

• Mixed with other lung disease, such as bronchiectasis, asthma, fibrosis, or emphysema

For these reasons, imaging must always be interpreted in full clinical context and often benefits from specialist multidisciplinary discussion.

Where AI may add particular value

AI does not “know” that it is looking at aspergillosis. Instead, it compares imaging data directly, without assumptions about diagnosis. This can be helpful in several areas relevant to aspergillosis:

1. Consistent comparison of serial scans
AI can compare computed tomography (CT) scans over time using the same criteria each time, helping to:

• Detect subtle interval change in cavities or nodules

• Measure changes in cavity size, wall thickness, or internal content

• Identify progression or stability that may be difficult to judge visually

This is particularly useful in chronic pulmonary aspergillosis, where progression may be slow and subtle.

2. Objective measurement of disease burden
AI can assist with:

• Quantifying cavity volume or consolidation

• Measuring extent of bronchiectasis or mucus plugging

• Tracking changes following antifungal treatment or airway clearance

Objective measurements may help reduce subjectivity when monitoring response to treatment.

3. Highlighting areas for closer review
AI systems can flag areas of change or abnormality for radiologist attention. This may act as a “second set of eyes”, particularly in busy services, but does not replace expert review.

What AI cannot do in aspergillosis

It is important to be clear about the limitations:

• AI cannot diagnose aspergillosis

• AI cannot distinguish colonisation from active disease

• AI cannot integrate symptoms, immune status, serology, or microbiology

• AI cannot judge clinical significance or treatment need

For example, a change in cavity appearance may reflect active disease, treatment response, bacterial infection, bleeding, or simple movement of intracavitary material. Only expert clinical interpretation can determine significance.

Why radiologist expertise remains essential

In aspergillosis, small imaging changes can have very different meanings depending on context. Radiologists bring:

• Experience in recognising mimics and artefacts

• Understanding of treatment-related change

• Ability to communicate uncertainty and recommend next steps

• Integration of imaging with wider clinical information

AI may improve consistency and sensitivity, but responsibility for interpretation and reporting remains with the radiologist.

A balanced way to think about AI in aspergillosis imaging

In aspergillosis, artificial intelligence is best viewed as a tool that highlights and measures change, rather than one that explains or diagnoses it. Its strength lies in consistency; its limitation lies in lack of clinical understanding.

Used appropriately, AI may support safer and more consistent follow-up for people living with aspergillosis, while expert radiology and specialist clinical care remain central to diagnosis and management.