Staged reflexive artificial intelligence driven testing algorithms for early diagnosis of pituitary disorders.

Journal: Clinical biochemistry
PMID: 34437886

Abstract

BACKGROUND: Sellar masses (SM) frequently present with insidious hormonal dysfunction. We previously showed that, by utilizing a combined reflex/reflecting approach involving a laboratory clinician (LC) on common endocrine test results requested by non-specialists, and subsequently adding further warranted tests, previously undiagnosed pituitary disorders can be identified. However, manually employing these strategies by an LC is not feasible for wider screening of pituitary disorders.

Authors

  • William Van Woensel
    NICHE Research Group, Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada. Electronic address: william.van.woensel@dal.ca.
  • Manal Elnenaei
    Department of Pathology and Laboratory Medicine, Nova Scotia Health Authority, Dalhousie University, Halifax, NS, Canada.
  • Syed Sibte Raza Abidi
  • David B Clarke
    Division of Neurosurgery, Dalhousie University, Halifax, NS Canada.
  • Syed Ali Imran
    Division of Endocrinology, Dalhousie University, Halifax, NS Canada. Electronic address: simran@dal.ca.

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