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Collins et al. Art Int Surg. 2025;5:254-69 https://dx.doi.org/10.20517/ais.2024.92 Page 262

Another major challenge in emergency settings is deciding whether to use prosthetic mesh for hernia repair,
particularly in contaminated fields where the risk of infection is high. In elective procedures, careful patient
selection and preoperative optimization help mitigate these risks, but in emergencies, there is often little
time for such preparation. AI-driven predictive models could help guide decision making by analyzing
patient-specific risk factors, surgical conditions, and historical outcomes to suggest the safest and most
effective repair approach.

Patient fragility is another key factor influencing surgical outcomes in emergencies. Many patients
presenting with abdominal wall emergencies are elderly or have significant comorbidities such as diabetes,
cardiovascular disease, or immunosuppression, making them more vulnerable to complications. AI-based
risk stratification tools could assist surgeons in determining the best course of action, balancing the risks of
immediate surgery against the potential for conservative management in high-risk patients.

Postoperative complications are more common in emergency AWS than in elective cases. Issues such as
SSIs, wound dehiscence, and anastomotic leaks can significantly prolong hospital stays and increase
morbidity. AI-driven early warning systems, which continuously monitor vital signs, laboratory results, and
other patient data, could help detect complications at an earlier stage, allowing for timely intervention and
potentially improving patient outcomes.

Postoperative care
The postoperative phase is crucial for ensuring smooth recovery and early detection of complications
following AWS, particularly in hernia repair and reconstruction. There has been limited research in this
area specific to AWS; however, we expect systems to be developed focusing on the following key areas:

Vital Sign Monitoring: AI algorithms can analyze real-time data from wearable sensors to track key
[48]
physiological parameters, such as heart rate, oxygen saturation, and body temperature . In the context of
AWS, they could be valuable tools for early detection of complications like infection or respiratory distress.

Wound Assessment: Postoperative wound care is critical in hernia repair to prevent complications such as
infection or poor healing. AI-driven image recognition tools can be used to monitor the surgical site,
detecting early signs of infection, dehiscence (wound opening), or excessive swelling [49-51] . For instance, AI
can analyze visual data from wound images to identify abnormal signs like increased redness, warmth, or
fluid accumulation, allowing clinicians to intervene early and reduce the risk of wound-related
complications.

Mesh Complication Detection: Mesh complications in hernia repairs can include abscesses, hematomas,
seromas, fistulas, bowel obstructions, mesh retraction, granulomas, and recurrent hernias. Imaging
techniques, particularly ultrasound and CT , are valuable tools for diagnosing these complications at
[52]
[53]
earlier stages, allowing for timely intervention and adjustment of treatment plans. Over the past few years,
the integration of AI into medical imaging has significantly transformed healthcare . We believe these
[54]
technologies should be incorporated into routine postoperative examinations for mesh complication
detection. By doing so, AI can enhance detection rates, reduce the burden on expert radiologists, and
improve overall efficiency in patient care.

Personalized Recovery Plans and Patient Follow-up: By leveraging AI for personalized recovery planning,
postoperative care for abdominal wall reconstruction and hernia repair patients can be more efficient and
tailored to individual recovery processes. AI-driven models analyze factors such as preoperative health,