Page 31 - Read Online

Page 419 Talwar et al. Art Int Surg. 2025;5:418-24 https://dx.doi.org/10.20517/ais.2025.22

following hernia formation, aiming to restore the native abdomen’s structure and function. These
procedures demand precise preoperative planning, accurate intraoperative execution, and effective
postoperative monitoring. Recent advancements in artificial intelligence (AI), particularly in image analysis
and computer vision (CV), have the potential to revolutionize medical imaging and enhance outcomes in
VHR. By leveraging AI algorithms, clinicians can gain deeper insights from medical imaging data, improve
risk stratification, and streamline decision-making processes. The purpose of this manuscript is to highlight
both the current applications of AI in medical image analysis for VHR and the future potential of this field
[Figure 1].

APPLICATIONS
Understanding pathophysiology
Incisional hernias (IH) are a common, morbid, long-term complication after abdominal surgery. They
occur in approximately 1%-10% of all abdominal surgeries, and they are increasingly common in the United
States and globally . Advancements in artificial intelligence (AI) present an opportunity to deepen the
[1,2]
[3]
understanding of hernia pathophysiology, an area that remains incompletely understood .
McAuliffe et al. utilized preoperative computed tomography (CT) imaging to train a deep learning (DL)
model to identify linear and volumetric measurements of viscera, adipose, and muscle in the
abdominopelvic cavity. Their work showed that widening of the rectus complex, increased visceral volume,
[4]
and abdominopelvic skeletal muscle atrophy were associated with the eventual development of IH . These
findings suggest a common pathophysiologic mechanism for developing ventral hernia, rooted in increased
intra-abdominal volume or pressure against a weakened abdominal wall.

Surgeons can use this work to preoperatively assess radiological images to evaluate the risk of developing
hernia. In their follow-up work, Talwar et al. used an extended dataset and employed three machine
learning (ML) techniques - Ensemble Boosting, Random Forest, and support vector machine - to identify
optimal CT-based biomarkers (OBMs) that could predict IH formation . The study found a parsimonious
[3]
model of three OBMs achieved a predictive accuracy of 0.83, underscoring the potential of AI to synthesize
[3]
imaging data and identify patterns linked to hernia formation . These findings lay the groundwork for
future studies using preoperative CT scans for risk stratification in VHR.

Preoperative surgical planning for VHR
Advancements in AI-based imaging have demonstrated significant potential in enhancing preoperative
planning for VHR.

Love et al. introduced a method to predict the need for additional myofascial release (AMR) during open
Rives-Stoppa hernia repair based on the rectus width to hernia width ratio (RDR) measured from CT
scans . Their approach relied on manual measurements of key anatomical features and was shown to be a
[5]
practical tool, showing a linear decrease in likelihood of requiring AMR as RDR increased . Though useful
[5]
for planning, 10% of patients with the highest RDR still required AMR. Additionally, the model failed to
account for key biomechanical features, like the elasticity of the abdominal wall, underscoring the need for
tools that leverage AI and CV to provide advanced insights into surgical planning.

Building on this foundation, Elhage et al. applied DL models to enhance preoperative planning for patients
undergoing abdominal wall reconstruction (AWR), a procedure to restore complex abdominal hernias.
They developed three convolutional neural network (CNN) models to predict the need for component
separation, surgical site infections (SSIs), and pulmonary complications . CNNs consist of multiple layers
[6,7]