Deep Learning Study of Alkaptonuria Spinal Disease Assesses Global and Regional Severity and Detects Occult Treatment Status

Abstract 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. Competing Interest Statement The authors have declared no competing interest. Funding Statement This research was supported by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health (ZIA HG200406 to B.D.S). It was also supported in part by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (ZIA HD009024 to C.R.F.). This work utilized the computational resources of the NIH HPC Biowulf cluster. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Applicable portions of the study have received ethical approval by the ethics committee/IRB of the National Institutes of Health#: 00HG0141, 05HG0076, 000547, and 002349, and appropriate consent was obtained from all research participants in accordance with the approved protocols. These NIH IRB protocols correspond to the original 20-year clinical trial of individuals with AKU (00HG0141), the clinical trial of individuals on nitisinone treatment (05HG0076), the analyses done in this study including using deep learning to analyze these images (000547), and the survey exemption protocol to send out surveys of these images to clinical geneticists and radiologists (002349). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Data Availability Data and code availability are described in the manuscript and can be found at: https://github.com/flahartyka/AKU-progression-efficientnet. We do not release the original dataset of AKU radiographs for patient privacy reasons and related to IRB requirements. If readers are interested in accessing the AKU dataset, they can contact the investigators of the clinical trial (00HG0141/NCT00005909), some of whom are co-authors on this study.