Deep Learning Study of Alkaptonuria Spinal Disease Assesses Global and Regional Severity and Detects Occult Treatment Status Article Swipe
Kendall A. Flaharty
,
V. Chandrasekar
,
Inmaculada Castillo
,
Dat Duong
,
Carlos R. Ferreira
,
Suzanna Ledgister Hanchard
,
Ping Hu
,
Rebekah L. Waikel
,
Francis Rossignol
,
Wendy J. Introne
,
Benjamin D. Solomon
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.11.25323762
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.11.25323762
Deep learning (DL) is increasingly used to analyze medical imaging, but is less refined for rare conditions, which require novel pre-processing and analytical approaches. To assess DL in the context of rare diseases, we focused on alkaptonuria (AKU), a rare disorder that affects the spine and involves other sequelae; treatments include the medication nitisinone. Since assessing X-rays to determine disease severity can be a slow, manual process requiring considerable expertise, we aimed to determine whether our DL methods could accurately identify overall spine severity, severity at specific regions of the spine, and whether DL could detect whether patients were receiving nitisinone. We evaluated DL performance versus clinical experts using cervical and lumbar spine radiographs. DL models predicted global severity scores (30-point scale) within 1.72 ± 1.96 points of expert clinician scores for cervical and 2.51 ± 1.96 points for lumbar radiographs. For region-specific metrics, we assessed the degree of narrowing, calcium, and vacuum phenomena at each intervertebral space (IVS). Our model’s narrowing scores were within 0.191-0.557 points from clinician scores (6-point scale), calcium was predicted with 78–90% accuracy (present, absent, or disc fusion), while vacuum disc phenomenon predictions were less consistent (41–90%). Intriguingly, DL models predicted nitisinone treatment status with 68–77% accuracy, while expert clinicians appeared unable to discern nitisinone status (51% accuracy) (p = 2.0 × 10 -9 ). This highlights the potential for DL to augment certain types of clinical assessments in rare disease, as well as identifying occult features like treatment status.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.03.11.25323762
- https://www.medrxiv.org/content/medrxiv/early/2025/03/12/2025.03.11.25323762.full.pdf
- OA Status
- green
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408403117
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- OpenAlex ID
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https://openalex.org/W4408403117Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.03.11.25323762Digital Object Identifier
- Title
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Deep Learning Study of Alkaptonuria Spinal Disease Assesses Global and Regional Severity and Detects Occult Treatment StatusWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-03-12Full publication date if available
- Authors
-
Kendall A. Flaharty, V. Chandrasekar, Inmaculada Castillo, Dat Duong, Carlos R. Ferreira, Suzanna Ledgister Hanchard, Ping Hu, Rebekah L. Waikel, Francis Rossignol, Wendy J. Introne, Benjamin D. SolomonList of authors in order
- Landing page
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https://doi.org/10.1101/2025.03.11.25323762Publisher landing page
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https://www.medrxiv.org/content/medrxiv/early/2025/03/12/2025.03.11.25323762.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2025/03/12/2025.03.11.25323762.full.pdfDirect OA link when available
- Concepts
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Alkaptonuria, Occult, Medicine, Disease, Internal medicine, Pathology, Dermatology, Alternative medicineTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- References (count)
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20Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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