AI model aims to predict ABPA relapse risk
NCT ID NCT07611838
First seen May 29, 2026 · Last updated Jun 23, 2026 · Updated 5 times
Summary
This study is developing a machine learning model to predict the risk of relapse within one year for people with allergic bronchopulmonary aspergillosis (ABPA), a lung condition caused by an allergic reaction to a fungus. Researchers will collect data from 300 patients across multiple hospitals to build and test the model. The goal is to help doctors identify high-risk patients and make better treatment decisions.
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Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
This is a summary of the original study . Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
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Contacts and locations
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Study contacts
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Contact
Phone: •••-•••-•••• Email: •••••@•••••
Locations
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Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, #16766, Jingshi Road, Jinan City, Shandong Province, China, Jinan, Shandong 250014
RECRUITINGJinan, Shandong, 250014, China
Contact Phone: •••-•••-•••• Email: •••••@•••••
What this could mean
Our plain-language read of the trial. This is informational only — not medical advice or a prediction.
What this could lead to
If successful, this could lead to a tool that helps doctors predict which ABPA patients are at higher risk of relapse, enabling more personalized monitoring and treatment.
What could go wrong
This is an observational study using existing data, not testing a new treatment. The model may not be accurate enough for real-world use, and results may not apply to all patients.
Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.