ArticleMycopathologia2021
Using Artificial Intelligence in Fungal Lung Disease: CPA CT Imaging as an Example.
Article in Mycopathologia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Central Nervous System Aspergillosis: Advances in Diagnosis, Therapeutics, and Multidisciplinary Management (2026 Update).Mycoses · 2026Review
- Advancements in Artificial Intelligence-Based Diagnostic Tools Used to Detect Fungal Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Invasive Mucormycosis in a Chronic Lymphocytic Leukemia Patient on Zanubrutinib: A Case Report.Infection and drug resistance · 2025Article
- Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders.Open life sciences · 2023Article
- Respiratory Mycoses: A Call to Action to Recognize, Educate and Invest.Mycopathologia · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This positioning paper aims to discuss current challenges and opportunities for artificial intelligence (AI) in fungal lung disease, with a focus on chronic pulmonary aspergillosis and some supporting proof-of-concept results using lung imaging. Given the high uncertainty in fungal infection diagnosis and analyzing treatment response, AI could potentially have an impactful role; however, developing imaging-based machine learning raises several specific challenges. We discuss recommendations to engage the medical community in essential first steps towards fungal infection AI with gathering dedicated imaging registries, linking with non-imaging data and harmonizing image-finding annotations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.