Application of neural networks in silicone breast implant diagnostics on magnetic resonance imaging

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F24%3A43927832" target="_blank" >RIV/00064173:_____/24:43927832 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/24:00378437 RIV/46747885:24210/24:00013026 RIV/00216208:11120/24:43927832

Result on the web

<a href="https://doi.org/10.14311/CTJ.2024.3.02" target="_blank" >https://doi.org/10.14311/CTJ.2024.3.02</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/CTJ.2024.3.02" target="_blank" >10.14311/CTJ.2024.3.02</a>

Result language

angličtina

Original language name

Application of neural networks in silicone breast implant diagnostics on magnetic resonance imaging

Original language description

Breast augmentation is one of the most frequently performed cosmetic procedures worldwide, but it carries certain risks including breast implant rupture. Timely and accurate diagnostics of ruptures are crucial, as undiagnosed ruptures can lead to serious health complications. Imaging methods, such as magnetic resonance imaging (MRI), are recommended for the diagnosis of breast implants due to their high accuracy. However, current diagnostics rely heavily on the subjective interpretation and experience of the physician. This study investigates the potential of neural networks (NN) to address this limitation and improve the accuracy of rupture detection in silicone breast implants. We applied a deep learning-based neural network system trained on MRI images of breast implants to detect ruptures. The dataset included annotated MRI scans of symptomatic and asymptomatic patients with confirmed implant integrity or rupture. Several models were trained using ResNet-18, ResNet-50, and Xception networks, with various hyperparameter settings and augmentation techniques applied to enhance model performance and generalizability. The performance of the NN model was evaluated using confusion matrices and standard metrics such as true positive rate (TPR) and true negative rate (TNR). A semi-automated algorithm for the detection of intracapsular ruptures of breast implants on MRI was successfully developed. The algorithm correctly detected ruptures in 95.4% of cases and accurately identified cases without rupture in 86.7% of instances. Our findings highlight the potential of neural networks as a supportive tool in diagnosing breast implant ruptures. By semi-automating rupture detection, NNs can reduce diagnostic errors, expedite image evaluation, and optimize resource use in medical practice. The study underscores the importance of combining artificial intelligence with expert evaluation to enhance patient care and reduce costs in medical diagnostics.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

30224 - Radiology, nuclear medicine and medical imaging

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Lékař a technika

ISSN

0301-5491

e-ISSN

2336-5552

Volume of the periodical

54

Issue of the periodical within the volume

3

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

6

Pages from-to

82-87

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85212794394