SynthesisHernia : the journal of hernias and abdominal wall surgery2024
Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review.
Synthesis in Hernia : the journal of hernias and abdominal wall surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Toward precision hernia surgery: integrating biomarkers, genomics, quantitative imaging, and artificial intelligence in abdominal wall repair.Hernia : the journal of hernias and abdominal wall surgery · 2026Review
- Artificial intelligence in abdominal wall hernia surgery: clinical applications and translational readiness.Surgical endoscopy · 2026Review
- Mapping the global landscape of robot-assisted hernia surgery research: a bibliometric analysis.Journal of robotic surgery · 2026Article
- Large registries, rare outcomes and neutral/inconclusive machine learning results in abdominal wall surgery.Journal of abdominal wall surgery : JAWS · 2026Article
- From repair to reconstruction: a holistic perspective in abdominal wall hernia surgery.Frontiers in surgery · 2026Article
- Deep learning in ventral hernia imaging: automated multi-structure CT segmentation for surgical planning.Journal of abdominal wall surgery : JAWS · 2026Article
- Impact of an AI workshop on knowledge and attitudes toward AI in scientific publishing among surgeons at an international abdominal wall surgery congress.Journal of abdominal wall surgery : JAWS · 2026Article
- Machine learning-based prediction of postoperative mortality risk after abdominal surgery.World journal of gastrointestinal surgery · 2025Article
- Optimizing Urological Concurrent Robotic Multisite Surgery: Juxtaposing a Single-Center Experience and a Literature Review.Journal of personalized medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThis systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.
methodsThe PRISMA guidelines were followed throughout this systematic review. The ROBINS-I and Rob 2 tools were used to perform qualitative assessment of all studies included in this review. Recommendations were then summarized for the following pre-defined key items: protocol, research question, search strategy, study eligibility, data extraction, study design, risk of bias, publication bias, and statistical analysis.
resultsA total of 13 articles were ultimately included for this review, describing the use of machine learning and deep learning for hernia surgery. All studies were published from 2020 to 2023. Articles varied regarding the population studied, type of machine learning or Deep Learning Model (DLM) used, and hernia type. Of the thirteen included studies, all included either inguinal, ventral, or incisional hernias. Four studies evaluated recognition of surgical steps during inguinal hernia repair videos. Two studies predicted outcomes using image-based DMLs. Seven studies developed and validated deep learning algorithms to predict outcomes and identify factors associated with postoperative complications.
conclusionThe use of ML for abdominal wall reconstruction has been shown to be a promising tool for predicting outcomes and identifying factors that could lead to postoperative complications.
Indexed as
Identifiers
38761300What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.