In:
Journal of Hematology & Oncology, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2020-12)
Abstract:
Due to acromegaly’s insidious onset and slow progression, its diagnosis is usually delayed, thus causing severe complications and treatment difficulty. A convenient screening method is imperative. Based on our previous work, we herein developed a new automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning on the data of 2148 photographs at different severity levels. Each photograph was given a score reflecting its severity (range 1~3). Our developed model achieved a prediction accuracy of 90.7% on the internal test dataset and outperformed the performance of ten junior internal medicine physicians (89.0%). The prospect of applying this model to real clinical practices is promising due to its potential health economic benefits.
Type of Medium:
Online Resource
ISSN:
1756-8722
DOI:
10.1186/s13045-020-00925-y
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2020
detail.hit.zdb_id:
2429631-4
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