Staged reflexive artificial intelligence driven testing algorithms for early diagnosis of pituitary disorders.
Clin Biochem
; 97: 48-53, 2021 Nov.
Article
in En
| MEDLINE
| ID: mdl-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.OBJECTIVE:
The aim of this study was to compare the accuracy and financial impact of an Artificial Intelligence (AI) based, fully computerized reflex protocol with manual reflex/reflective intervention protocol led by an LC.METHODS:
We developed a proof-of-concept AI-based framework to fully computerize multi-stage reflex testing protocols for pituitary dysfunction using automated reasoning methods. We compared the efficacy of this AI-based protocol with a reflex/reflective protocol based on manually curated retrospective data in identifying pituitary dysfunction based on 12 months of laboratory testing.RESULTS:
The AI-based reflex protocol, as compared with the manual protocol, would have identified laboratory tests for add-on that either directly matched or included all manual add-on tests in 92% of cases, and recommended a similar specialist referral in 90% of the cases. The AI-based protocol would have issued 2.8 times the total number of manual add-on laboratory tests at an 85% lower operation cost than the manual protocol when considering marginal test costs, technical staff and specialist salary. CONCLUSION/DISCUSSION:
Our AI-based reflex protocol can successfully identify patients with pituitary dysfunction, with lower estimated laboratory cost. Future research will focus on enhancing the protocol's accuracy and incorporating the AI-based reflex protocol into institutional laboratory and hospital information systems for the detection of undiagnosed pituitary disorders.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Pituitary Diseases
/
Artificial Intelligence
/
Diagnosis, Computer-Assisted
Type of study:
Diagnostic_studies
/
Guideline
/
Observational_studies
/
Prognostic_studies
/
Screening_studies
Limits:
Female
/
Humans
/
Male
/
Middle aged
/
Pregnancy
Language:
En
Journal:
Clin Biochem
Year:
2021
Document type:
Article
Affiliation country: