AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification Article Swipe
Daniel Csillag
,
Lucas Monteiro Paes
,
Thiago Ramos
,
João Vitor Romano
,
R. B. Schüller
,
Roberto B. Seixas
,
Roberto I. Oliveira
,
Paulo Orenstein
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i13.26837
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i13.26837
Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages deep learning and conformal prediction to output fast and accurate volume estimates and segmentation masks from fetal MRIs with Dice coefficient over 0.9. Also, we make available a novel, curated dataset for fetal MRIs with 853 exams and benchmark the performance of many recent deep learning architectures. In addition, we introduce a conformal prediction tool that yields narrow predictive intervals with theoretically guaranteed coverage, thus aiding doctors in detecting pregnancy risks and saving lives. A successful case study of AmnioML deployed in a medical setting is also reported. Real-world clinical benefits include up to 20x segmentation time reduction, with most segmentations deemed by doctors as not needing any further manual refinement. Furthermore, AmnioML's volume predictions were found to be highly accurate in practice, with mean absolute error below 56mL and tight predictive intervals, showcasing its impact in reducing pregnancy complications.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v37i13.26837
- https://ojs.aaai.org/index.php/AAAI/article/download/26837/26609
- OA Status
- diamond
- Cited By
- 7
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382318443
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Raw OpenAlex JSON
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https://openalex.org/W4382318443Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v37i13.26837Digital Object Identifier
- Title
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AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty QuantificationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-26Full publication date if available
- Authors
-
Daniel Csillag, Lucas Monteiro Paes, Thiago Ramos, João Vitor Romano, R. B. Schüller, Roberto B. Seixas, Roberto I. Oliveira, Paulo OrensteinList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v37i13.26837Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/26837/26609Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/26837/26609Direct OA link when available
- Concepts
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Segmentation, Benchmark (surveying), Computer science, Deep learning, Artificial intelligence, Volume (thermodynamics), Task (project management), Machine learning, Sørensen–Dice coefficient, Image segmentation, Management, Geography, Economics, Geodesy, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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