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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

30224 - Radiology, nuclear medicine and medical imaging

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Lékař a technika

ISSN

0301-5491

e-ISSN

2336-5552

Svazek periodika

54

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

82-87

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85212794394