An algorithm as a diagnostic tool for central ocular motor disorders, also to diagnose rare disorders

Orphanet J Rare Dis. 2019 Aug 8;14(1):193. doi: 10.1186/s13023-019-1164-8.

Abstract

Background: Recently an increasing number of digital tools to aid clinical work have been published. This study's aim was to create an algorithm which can assist physicians as a "digital expert" with the differential diagnosis of central ocular motor disorders, in particular in rare diseases.

Results: The algorithm's input consists of a maximum of 60 neurological and oculomotor signs and symptoms. The output is a list of the most probable diagnoses out of 14 alternatives and the most likely topographical anatomical localizations out of eight alternatives. Positive points are given for disease-associated symptoms, negative points for symptoms unlikely to occur with a disease. The accuracy of the algorithm was evaluated using the two diagnoses and two brain zones with the highest scores. In a first step, a dataset of 102 patients (56 males, 48.0 ± 22 yrs) with various central ocular motor disorders and underlying diseases, with a particular focus on rare diseases, was used as the basis for developing the algorithm iteratively. In a second step, the algorithm was validated with a dataset of 104 patients (59 males, 46.0 ± 23 yrs). For 12/14 diseases, the algorithm showed a sensitivity of between 80 and 100% and the specificity of 9/14 diseases was between 82 and 95% (e.g., 100% sensitivity and 75.5% specificity for Niemann Pick type C, and 80% specificity and 91.5% sensitivity for Gaucher's disease). In terms of a topographic anatomical diagnosis, the sensitivity was between 77 and 100% for 4/8 brain zones, and the specificity of 5/8 zones ranged between 79 and 99%.

Conclusion: This algorithm using our knowledge of the functional anatomy of the ocular motor system and possible underlying diseases is a useful tool, in particular for the diagnosis of rare diseases associated with typical central ocular motor disorders, which are often overlooked.

Keywords: Algorithm; Ataxia teleangiectasia; Ataxia with oculomotor apraxia; Gaucher’s disease type 3; Niemann pick type C; Ocular motor disorder; Progressive supranuclear palsy; Wernicke encephalopathy.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Algorithms*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Motor Disorders / diagnosis
  • Niemann-Pick Diseases / diagnosis
  • Rare Diseases / diagnosis*
  • Young Adult