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Adrenal Gland Disorders

Adrenal Gland Disorders

Adrenal gland disorders manifest when the production of adrenal hormones deviates from the norm, resulting in either excessive or insufficient concentrations.
Adrenal insufficiency, characterized by insufficient cortisol and aldosterone production, leads to conditions like Addison's disease. This disorder, affecting the adrenal cortex, exhibits symptoms such as skin bronzing, dehydration, low blood pressure, fatigue, and weight loss. Congenital adrenal hyperplasia, a genetic ailment causing...

Anatomy of the Adrenal Glands

Anatomy of the Adrenal Glands

The adrenal or supra-renal glands, situated above the kidneys and aligned with the twelfth rib, are paired pyramid-shaped structures crucial for the body's stress response. During stress, these glands secrete hormones vital for adaptive physiological reactions.
These glands possess a distinctive yellow tinge due to the stored cholesterol and fatty acids required for hormone synthesis. They are encased in a fibrous capsule and cushioned by fat.
The adrenal gland comprises two distinct...

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A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.

Wasif Bala¹, Jackson Steinkamp¹, Timothy Feeney¹

¹Boston Medical Center, One Boston Medical Center Pl, Boston, Massachusetts, United States.

Applied Clinical Informatics

|September 16, 2020

Summary

A new machine learning algorithm and web application can identify and manage adrenal incidentalomas from radiology reports. This tool improves patient follow-up for these common incidental findings.

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Clinical Informatics

Background:

Incidental radiographic findings, like adrenal nodules, are frequent but often lack appropriate patient follow-up.
Lack of management tools and time constraints hinder tracking of incidental findings.
Natural Language Processing (NLP) offers a solution for extracting data from clinical documents without workflow changes.

Purpose of the Study:

Develop a machine learning algorithm to detect newly discovered adrenal incidentalomas in radiology reports.
Create a web application for real-time management of identified adrenal incidentalomas.

Main Methods:

Manually annotated 4,090 radiology reports for the presence of newly discovered adrenal incidentalomas.
Trained a convolutional neural network for text classification of reports.
Built a web application utilizing the NLP model for clinical management coordination.

Main Results:

The model achieved high performance: 92.9% sensitivity, 83.0% positive predictive value, 97.8% specificity, and 87.6% F1 score.
A functional web application was developed based on the model's output.
The dataset comprised 404 positive and 3,686 negative reports.

Conclusions:

An NLP-enabled web application for adrenal incidentaloma management is feasible, even in resource-constrained settings.
This tool can support quality improvement departments and primary care providers.
The approach is generalizable to managing other types of clinical findings.