# Developing Database and Software infrastructure for Quantitative Radiologic Analysis of Lumbar Radiculopathy

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $195,000

## Abstract

Project Summary/Abstract
Diagnosis of lumbar radiculopathy (LR) currently relies on a qualitative interpretation of magnetic resonance
imaging (MRI) studies and lacks standardization. This has led to inconsistent treatment and rising costs, while
quality of life metrics have remained stagnant. To standardize the diagnosis of LR, the subjective and qualitative
radiologic assessment needs to be augmented with accurate measurements of neuroforamina (NF) and central
canal (CC) areas, two anatomical structures that are critical to the etiology of LR. However, precise
measurements will require manual delineations of these regions on MRI. This is a tedious and time-consuming
process that is not feasible on a daily, large-scale basis in the clinic. Deep Learning (DL) is a relatively new
machine learning technique, which holds the promise of automating NF and CC segmentation. None the less,
there remain several challenges to making DL-based segmentation routine in clinical practice. First, training and
validating a DL model for segmentation of a given anatomical structure requires a large amount of expert
annotated training data. Expert annotated data is expensive and time consuming to obtain, thus thwarting the
development of quantitative imaging diagnostics for LR. To address this, we propose an expert-led manual
delineation of NF and CC using de-identified MRI data extracted from UCLA's picture archiving and
communications system (PACS). We expect the resulting database to contain data from over 35,000 lumbar
MRI scans, with associated clinical history, demographics, and patient outcomes data. In a subset (1000) of
these data, NFs and CCs will be annotated by multiple human expert raters. The consensus of these delineations
will be used as ground truth segmentations to train, validate and improve our understanding of DL models.
Secondly, as a part of this proposal, we aim to address several technical challenges that limit the deployment of
automated image segmentation techniques to the clinic. Chief amongst these challenges is the failure of
automated methodologies in the face of variation due to factors such as pathology, scanner protocol alterations,
and general demographic variation. Additionally, our current understanding of DL does not allow us to
categorically state the total number of expert annotated data that will be needed to train a model with a specified
level of accuracy. Finally, we do not currently understand how selection of training cases for expert delineation
affects generalization accuracy. To address the aforementioned challenges, we propose experiments to define
the relationship between DL algorithms and the cardinality of training data. We will also explore the use of
unsupervised machine learning strategies, namely clustering and reinforcement learning, to understand how
training data selection influences algorithmic accuracy. In summary, we propose to address data availability and
technical knowledge gaps to the development of ...

## Key facts

- **NIH application ID:** 9928429
- **Project number:** 5R21EB026665-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Luke Macyszyn
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $195,000
- **Award type:** 5
- **Project period:** 2019-06-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9928429

## Citation

> US National Institutes of Health, RePORTER application 9928429, Developing Database and Software infrastructure for Quantitative Radiologic Analysis of Lumbar Radiculopathy (5R21EB026665-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9928429. Licensed CC0.

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