Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction

Deep learning predicts postoperative opioids refills in a multi-institutional cohort of surgical patients

BACKGROUND

To combat the opioid epidemic, several strategies were implemented to limit the unnecessary prescription of opioids in the postoperative period. However, this leaves a subset of patients who genuinely require additional opioids with inadequate pain control. Deep learning models are powerful tools with great potential of optimizing health care delivery through a patient-centered focus. We sought to investigate whether deep learning models can be used to predict patients who would require additional opioid prescription refills in the postoperative period after elective surgery.

METHODS

This is a retrospective study of patients who received elective surgical intervention at the Mayo Clinic. Adult English-speaking patients ≥18 years old, who underwent an elective surgical procedure between 2013 and 2019, were eligible for inclusion. Machine learning models, including deep learning, random forest, and eXtreme Gradient Boosting, were designed to predict patients who require opioid refills after discharge from hospital.

RESULTS

A total of 9,731 patients with mean age of 62.1 years (51.4% female) were included in the study. Deep learning and random forest models predicted patients who required opioid refills with high accuracy, 0.79 ± 0.07 and 0.78 ± 0.08, respectively. Procedure performed, highest pain score recorded during hospitalization, and total oral morphine milligram equivalents prescribed at discharge were the top 3 predictors for requiring opioid refills after discharge.

CONCLUSION

Deep learning models can be used to predict patients who require postoperative opioid prescription refills with high accuracy. Other machine learning models, such as random forest, can perform equal to deep learning, increasing the applicability of machine learning for combating the opioid epidemic.

Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging

BACKGROUND AND OBJECTIVES

Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy.

METHODS

Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined.

RESULTS

Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41.

CONCLUSIONS

Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.

Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up

BACKGROUND

Deep learning models (DLMs) using preoperative computed tomography (CT) imaging have shown promise in predicting outcomes following abdominal wall reconstruction (AWR), including component separation, wound complications, and pulmonary failure. This study aimed to apply these methods in predicting hernia recurrence and to evaluate if incorporating additional clinical data would improve the DLM's predictive ability.

METHODS

Patients were identified from a prospectively maintained single-institution database. Those who underwent AWR with available preoperative CTs were included, and those with < 18 months of follow up were excluded. Patients were separated into a training (80%) set and a testing (20%) set. A DLM was trained on the images only, and another DLM was trained on demographics only: age, sex, BMI, diabetes, and history of tobacco use. A mixed-value DLM incorporated data from both. The DLMs were evaluated by the area under the curve (AUC) in predicting recurrence.

RESULTS

The models evaluated data from 190 AWR patients with a 14.7% recurrence rate after an average follow up of more than 7 years (mean ± SD: 86 ± 39 months; median [Q1, Q3]: 85.4 [56.1, 113.1]). Patients had a mean age of 57.5 ± 12.3 years and were majority (65.8%) female with a BMI of 34.2 ± 7.9 kg/m2. There were 28.9% with diabetes and 16.8% with a history of tobacco use. The AUCs for the imaging DLM, clinical DLM, and combined DLM were 0.500, 0.667, and 0.604, respectively.

CONCLUSIONS

The clinical-only DLM outperformed both the image-only DLM and the mixed-value DLM in predicting recurrence. While all three models were poorly predictive of recurrence, the clinical-only DLM was the most predictive. These findings may indicate that imaging characteristics are not as useful for predicting recurrence as they have been for other AWR outcomes. Further research should focus on understanding the imaging characteristics that are identified by these DLMs and expanding the demographic information incorporated in the clinical-only DLM to further enhance the predictive ability of this model.

Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review

INTRODUCTION

This systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.

METHODS

The 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.

RESULTS

A 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.

CONCLUSION

The 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.

Computed tomography measurements to predict need for robotic transversus abdominis release: a single institution analysis

PURPOSE

The radiographic rectus width to hernia width ratio (RDR) has been shown to predict ability to close fascial defect without additional myofascial release in open Rives-Stoppa abdominal wall reconstruction (AWR), but it has not been studied in robotic AWR. We aimed to examine various CT measurements to determine their usability in predicting the need for transversus abdominis release (TAR) in robotic AWR.

METHODS

We performed a single-center retrospective review of 137 patients with midline ventral hernias over a 5-year period who underwent elective robotic retrorectus AWR. We excluded patients with M1 or M5 hernias, lateral/flank hernias, and hybrid repairs. The CT measurements included hernia width (HW), hernia width/abdominal width ratio (HW/AW), and RDR. Univariate, multivariate and area under the curve (AUC) analyses were performed.

RESULTS

58/137 patients required TAR (32 unilateral, 26 bilateral). Patients undergoing TAR had a significantly higher average HW and HW/AW and lower RDR. Multivariate analysis revealed that prior hernia repair was independently associated with need for TAR (p = 0.03). ROC analysis and AUC values showed acceptable diagnostic ability of HW, HW/AW and RDR in predicting need for TAR. Cutoffs of RDR ≤ 2, HW/AW > 0.3, and HW > 10 cm yielded high specificity in determining need for any TAR (97.5% vs. 96.2% vs. 92.4%) or bilateral TAR (95.5% vs. 94.6% vs. 92.8%).

CONCLUSION

History of prior hernia repair was a risk factor for robotic TAR. CT measurements have some predictive value in determining need for TAR in robotic AWR. Further prospective analysis is needed in this patient population.

Assessing Patient-Reported Experiences for In-Person and Telemedicine-Based Preoperative Evaluations

Background: The COVID-19 pandemic has transformed health care delivery through the rise of telehealth solutions. Though telemedicine-based care has been identified as safe and feasible in postoperative care, data on initial surgical consultations in the preoperative setting are lacking. We sought to compare patient characteristics, anticipated downstream care utilization, and patient-reported experiences (PREs) for in-person versus telemedicine-based care conducted for initial consultation encounters at a hernia and abdominal wall center. Methods: Patients evaluated at an abdominal wall reconstruction center from August 2021 to August 2022 were prospectively surveyed. Patient characteristics, anticipated downstream care utilization, and PREs were compared. Results: Of the 176 respondents, 50.6% (n = 89) utilized telemedicine-based care and had similar demographic and disease characteristics to those receiving in-person care. Telemedicine-based care saved a median of 47 min [interquartile range 20-112.5 min] of round-trip travel time per patient, with 10.1% of encounters resulting in supplemental in-person evaluation. A large proportion of telemedicine-based and in-person encounters resulted in recommendations for operative intervention, 38.2% versus 55.2%, respectively. Indirect costs of care were significantly lower for patients utilizing telemedicine-based services. Patient satisfaction related to encounters was non-inferior to in-person care. Overall, the majority of patients responded that they preferred future care to be delivered via telemedicine-based services, if offered. Conclusions: Preoperative telemedicine-based care was associated with significant cost-savings over in-person care related with comparable patient satisfaction. Health systems should continue to dedicate resources to optimizing and expanding perioperative telemedicine capabilities.

Development and Validation of an Automated Classification System for Osteonecrosis of the Femoral Head Using Deep Learning Approach: A Multicenter Study

BACKGROUND

Accurate classification can facilitate the selection of appropriate interventions to delay the progression of osteonecrosis of the femoral head (ONFH). This study aimed to perform the classification of ONFH through a deep learning approach.

METHODS

We retrospectively sampled 1,806 midcoronal magnetic resonance images (MRIs) of 1,337 hips from 4 institutions. Of these, 1,472 midcoronal MRIs of 1,155 hips were divided into training, validation, and test datasets with a ratio of 7:1:2 to develop a convolutional neural network model (CNN). An additional 334 midcoronal MRIs of 182 hips were used to perform external validation. The predictive performance of the CNN and the review panel was also compared.

RESULTS

A multiclass CNN model was successfully developed. In internal validation, the overall accuracy of the CNN for predicting the severity of ONFH based on the Japanese Investigation Committee classification was 87.8%. The macroaverage values of area under the curve (AUC), precision, recall, and F-value were 0.90, 84.8, 84.8, and 84.6%, respectively. In external validation, the overall accuracy of the CNN was 83.8%. The macroaverage values of area under the curve, precision, recall, and F-value were 0.87, 79.5, 80.5, and 79.9%, respectively. In a human-machine comparison study, the CNN outperformed or was comparable to that of the deputy chief orthopaedic surgeons.

CONCLUSION

The CNN is feasible and robust for classifying ONFH and correctly locating the necrotic area. These findings suggest that classifying ONFH using deep learning with high accuracy and generalizability may aid in predicting femoral head collapse and clinical decision-making.

[Roboticassisted incisional hernia surgery-Retromuscular techniques]

The trend to minimally invasive surgery has also made its way into the surgical treatment of incisional hernias. Unlike other areas of visceral surgery, recent years have seen a resurgence of open sublay repair in incisional hernia procedures, primarily due to the recognition of the retromuscular layer as the optimal mesh placement site. Additionally, with the growing availability of robotic systems in visceral surgery, these procedures are increasingly being offered in the form of minimally invasive procedures. These methods can be categorized based on the access routes: robotic-assisted transperitoneal procedures (e.g., r‑Rives, r‑TARUP, r‑TAR) and total extraperitoneal hernia repair (e.g., r‑eTEP, r‑eTAR). Notably, the introduction of transversus abdominis muscle release enables the robotic-assisted treatment of larger and more complex hernia cases with complete fascial closure. With respect to the comparison with open surgery required in retromuscular hernia treatment, the currently available literature on incisional hernia repair seems to show initial advantages of robotic-assisted surgery in the perioperative course. New technologies create new possibilities. In the context of surgical training the use of surgical robot systems with double consoles opens up completely new perspectives. Furthermore, the robot enables the implementation of models of artificial intelligence and augmented reality and could therefore open up novel dimensions in surgery.

Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning

BACKGROUND

Ureteral injury (UI) is a rare but devastating complication during colorectal surgery. Ureteral stents may reduce UI but carry risks themselves. Risk predictors for UI could help target the use of stents, but previous efforts have relied on logistic regression (LR), shown moderate accuracy, and used intraoperative variables. We sought to use an emerging approach in predictive analytics, machine learning, to create a model for UI.

METHODS

Patients who underwent colorectal surgery were identified in the National Surgical Quality Improvement Program (NSQIP) database. Patients were split into training, validation, and test sets. The primary outcome was UI. Three machine learning approaches were tested including random forest (RF), gradient boosting (XGB), and neural networks (NN), and compared with traditional LR. Model performance was assessed using area under the curve (AUROC).

RESULTS

The data set included 262,923 patients, of whom 1519 (.578%) experienced UI. Of the modeling techniques, XGB performed the best, with an AUROC score of .774 (95% CI .742-.807) compared with .698 (95% CI .664-.733) for LR. Random forest and NN performed similarly with scores of .738 and .763, respectively. Type of procedure, work RVUs, indication for surgery, and mechanical bowel prep showed the strongest influence on model predictions.

CONCLUSIONS

Machine learning-based models significantly outperformed LR and previous models and showed high accuracy in predicting UI during colorectal surgery. With proper validation, they could be used to support decision making regarding the placement of ureteral stents preoperatively.

Defining surgical risk in octogenarians undergoing paraesophageal hernia repair

BACKGROUND

With an aging population, the utility of surgery in elderly patients, particularly octogenarians, is of increasing interest. The goal of this study was to analyze outcomes of octogenarians versus non-octogenarians undergoing paraesophageal hernia repair (PEHR).

METHODS

The Nationwide Readmission Database was queried for patients > 18 years old who underwent PEHR from 2016 to 2018. Exclusion criteria included a diagnosis of gastrointestinal malignancy or a concurrent bariatric procedure. Patients ≥ 80 were compared to those 18-79 years old using standard statistical methods, and subgroup analyses of elective and non-elective PEHRs were performed.

RESULTS

From 2016 to 2018, 46,450 patients were identified with 5425 (11.7%) octogenarians and 41,025 (88.3%) non-octogenarians. Octogenarians were more likely to have a non-elective operation (46.3% vs 18.2%, p < 0.001), and those undergoing non-elective PEHR had a higher mortality (5.5% vs 1.2%, p < 0.001). Outcomes were improved with elective PEHR, but octogenarians still had higher mortality (1.3% vs 0.2%, p < 0.001), longer LOS (3[2, 5] vs 2[1, 3] days, p < 0.001), and higher readmission rates within 30 days (11.1% vs 6.5%, p < 0.001) compared to non-octogenarian elective patients. Multivariable logistic regression showed that being an octogenarian was not independently predictive of mortality (odds ratio (OR) 1.373[95% confidence interval 0.962-1.959], p = 0.081), but a non-elective operation was (OR 3.180[2.492-4.057], p < 0.001). Being an octogenarian was a risk factor for readmission within 30 days (OR 1.512[1.348-1.697], p < 0.001).

CONCLUSIONS

Octogenarians represented a substantial proportion of patients undergoing PEHR and were more likely to undergo a non-elective operation. Being an octogenarian was not an independent predictor of perioperative mortality, but a non-elective operation was. Octogenarians' morbidity and mortality was reduced in elective procedures but was still higher than non-octogenarians. Elective PEHR in octogenarians is reasonable but should involve a thorough risk-benefit analysis.

Preoperative Optimization for Abdominal Wall Reconstruction

Patients requiring abdominal wall reconstruction may have medical comorbidities and/or complex defects. Comorbidities such as smoking, diabetes, obesity, cirrhosis, and frailty have been associated with an increased risk of postoperative complications. Prehabilitation strategies are variably associated with improved outcomes. Large hernia defects and loss of domain may present challenges in achieving fascial closure, an important part of restoring abdominal wall function. Prehabilitation of the abdominal wall can be achieved with the use of botulinum toxin A, and preoperative progressive pneumoperitoneum.

The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential localization: an experimental study

BACKGROUND

Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and high-resolution abdominal computed tomography (CT) can adequately detect the injury. However, these lethal injuries sometimes have been overlooked in current practice. Deep learning (DL) algorithms have proven their capabilities in detecting abnormal findings in medical images. The aim of this study is to develop a three-dimensional, weakly supervised DL algorithm for detecting splenic injury on abdominal CT using a sequential localization and classification approach.

MATERIAL AND METHODS

The dataset was collected in a tertiary trauma center on 600 patients who underwent abdominal CT between 2008 and 2018, half of whom had splenic injuries. The images were split into development and test datasets at a 4 : 1 ratio. A two-step DL algorithm, including localization and classification models, was constructed to identify the splenic injury. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Grad-CAM (Gradient-weighted Class Activation Mapping) heatmaps from the test set were visually assessed. To validate the algorithm, we also collected images from another hospital to serve as external validation data.

RESULTS

A total of 480 patients, 50% of whom had spleen injuries, were included in the development dataset, and the rest were included in the test dataset. All patients underwent contrast-enhanced abdominal CT in the emergency room. The automatic two-step EfficientNet model detected splenic injury with an AUROC of 0.901 (95% CI: 0.836-0.953). At the maximum Youden index, the accuracy, sensitivity, specificity, PPV, and NPV were 0.88, 0.81, 0.92, 0.91, and 0.83, respectively. The heatmap identified 96.3% of splenic injury sites in true positive cases. The algorithm achieved a sensitivity of 0.92 for detecting trauma in the external validation cohort, with an acceptable accuracy of 0.80.

CONCLUSIONS

The DL model can identify splenic injury on CT, and further application in trauma scenarios is possible.

Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer

The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagen^DL) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagen^DL classifier was significantly associated with DFS and OS (P < 0.001). The collagen^DL nomogram, integrating the collagen^DL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagen^DL classifier, rather than low-collagen^DL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagen^DL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.

Predicting rare outcomes in abdominal wall reconstruction using image-based deep learning models

BACKGROUND

Deep learning models with imbalanced data sets are a challenge in the fields of artificial intelligence and surgery. The aim of this study was to develop and compare deep learning models that predict rare but devastating postoperative complications after abdominal wall reconstruction.

METHODS

A prospectively maintained institutional database was used to identify abdominal wall reconstruction patients with preoperative computed tomography scans. Conventional deep learning models were developed using an 8-layer convolutional neural network and a 2-class training system (ie, learns negative and positive outcomes). Conventional deep learning models were compared to deep learning models that were developed using a generative adversarial network anomaly framework, which uses image augmentation and anomaly detection. The primary outcomes were receiver operating characteristic values for predicting mesh infection and pulmonary failure.

RESULTS

Computed tomography scans from 510 patients were used with a total of 10,004 images. Mesh infection and pulmonary failure occurred in 3.7% and 5.6% of patients, respectively. The conventional deep learning models were less effective than generative adversarial network anomaly for predicting mesh infection (receiver operating characteristic 0.61 vs 0.73, P < .01) and pulmonary failure (receiver operating characteristic 0.59 vs 0.70, P < .01). Although the conventional deep learning models had higher accuracies/specificities for predicting mesh infection (0.93 vs 0.78, P < .01/.96 vs .78, P < .01) and pulmonary failure (0.88 vs 0.68, P < .01/.92 vs .67, P < .01), they were substantially compromised by decreased model sensitivity (0.25 vs 0.68, P < .01/.27 vs .73, P < .01).

CONCLUSION

Compared to conventional deep learning models, generative adversarial network anomaly deep learning models showed improved performance on imbalanced data sets, predominantly by increasing model sensitivity. Understanding patients who are at risk for rare but devastating postoperative complications can improve risk stratification, resource utilization, and the consent process.

Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review

Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41666-023-00129-2.

Perioperative Telemedicine Utilization Among Geriatric Patients Being Evaluated for Abdominal Wall Reconstruction and Hernia Repair

Introduction: Perioperative telemedicine services have increasingly been utilized for ambulatory care, although concerns exist regarding the feasibility of virtual consultations for older patients. We sought to review telemedicine encounters for geriatric patients evaluated at a hernia repair and abdominal wall reconstruction center. Methods: A retrospective review of telemedicine encounters between May 2020 and May 2021 was performed. Patient characteristics and encounter-specific outcomes were compared among geriatric (older than65 years old) and nongeriatric patients. Clinical care plans for encounters were reviewed to determine potential downstream care utilization. Patient-derived benefits related to time saved in travel time was calculated using geo-mapping. Outcomes for postoperative encounters were assessed to determine if complication rates differed between geriatric and nongeriatric populations. Results: A total of 313 telemedicine encounters (geriatric: 41.9%) were conducted among 251 patients. Reviewing preoperative factors for hernia care, geriatric patients presented with higher rates of recurrent or incisional hernias (87.9% vs. 70.7%, p < 0.01). Potential travel time was longer for geriatric patients (104 min vs. 42 min, p = 0.03) in the preoperative setting. No differences in clinical care plans were found. Only 8.6% of preoperative encounters resulted in recommendations for supplemental in-person evaluation. Operative plans were coordinated for 42.5% of all preoperative telemedicine encounters. There was no difference in complication rate between geriatric and nongeriatric patients (p > 0.05) in the postoperative setting, with no complications directly attributable to telemedicine-based care. Conclusions: Telemedicine-based evaluations appear to function well among geriatric patients seeking hernia repair and abdominal wall reconstruction. Clinical care plans rendered following telemedicine-based encounters are appropriate with a low rate of supplemental in-person evaluations. Telemedicine use resulted in significantly more time saved in commuting to and from clinic for geriatric patients.

Outcomes of Telemedicine-Based Consultation among Rural Patients Referred for Abdominal Wall Reconstruction and Hernia Repair

BACKGROUND

Perioperative telemedicine use has increased as a result of the COVID-19 pandemic and may improve access to surgical care. However, studies assessing outcomes in populations at risk for digital-health disparities are lacking. We sought to characterize the pre- and postoperative outcomes for rural patient populations being assessed for hernia repair and abdominal wall reconstruction with telehealth.

METHODS

Patients undergoing telehealth evaluation from March 2020 through May 2021 were identified. Rurality was identified by zip code of residence. Rural and urban patients were compared based on demographics, diagnosis, treatment plan, and visit characteristics and outcomes. Downstream care use related to supplementary in-person referral, and diagnostic testing was assessed.

RESULTS

Three hundred-seventy-three (196 preoperative, 177 postoperative) telehealth encounters occurred during the study period (rural: 28% of all encounters). Rural patients were more likely to present with recurrent or incisional hernias (90.0 vs 72.7%, p = 0.02) and advanced comorbidities (American Society of Anesthesiologists status score > 2: 73.1 vs 52.1%, p = 0.009). Rural patients derived significant benefits related to time saved commuting, with median distances of 299 and 293 km for pre- and postoperative encounters, respectively. Downstream care use was 6.1% (N = 23) for additional in-person evaluations and 3.4% (N = 13) for further diagnostic testing, with no difference by rurality.

CONCLUSIONS

Perioperative telehealth can safely be implemented for rural populations seeking hernia repair and may be an effective method for reducing disparities. Downstream care use related to additional in-person referral or diagnostic testing was minimally impacted in both the preoperative and postoperative settings. These findings suggest that rurality should not deter surgeons from providing telemedicine-based surgical consultation for hernia repair.

Telemedicine-based new patient consultations for hernia repair and advanced abdominal wall reconstruction

Purpose

Telemedicine has emerged as a viable option to in-person visits for the evaluation and management of surgical patients. Increased integration of telemedicine has allowed for greater access to care for specific patient populations but relative outcomes are unstudied. Given these limitations, we sought to evaluate the efficacy of telemedicine-based new patient preoperative encounters in comparison to in-person encounters.

Methods

We performed a retrospective analysis of adult patients undergoing new patient evaluations from April 2020 to October 2021. Telemedicine visits consist of both video and telephone-based encounters. Visit types, patient demographics, preoperative diagnosis, travel time to the hospital, and prior imaging availability were reviewed.

Results

A total of 276 new patient encounters were conducted (n = 108, 39% telemedicine). Indications for evaluation included inguinal hernia (n = 81, 30%), ventral hernia (n = 149, 54%) and groin or abdominal pain (n = 30, 11%). Patients undergoing telehealth evaluations were more likely to have greater travel distance to the hospital (91 km vs 29 km, p = 0.002) and have CT image-confirmed diagnoses at the initial visit (73 vs 47%, p < 0.001). Patients who were evaluated for a recurrent or incisional hernia were more likely to be seen through a telemedicine encounter (69 vs 45%, p < 0.001).

Conclusions

We report the efficacy of telemedicine-based consultations for new patient preoperative evaluations related to hernia repair and abdominal wall reconstruction. Telemedicine is a useful modality for preoperative evaluation of new patients with hernia and advanced abdominal wall reconstruction needs. Understanding this patient population will allow us to optimize telemedicine encounters for new patients and improve access to care for patients in remote locations.

The Development of Artificial Intelligence in Hernia Surgery: A Scoping Review

Background

Artificial intelligence simulates human intelligence in machines that have undergone programming to make them think like human beings and imitate their activities. Artificial intelligence has dominated the medical sector to perform various patient diagnosis activities and improve communication between professionals and patients. The main goal of this study is to perform a scoping review to evaluate the development of artificial intelligence in all forms of hernia surgery except the diaphragm and upside-down hernia.

Methods

The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to guide the structuring of the manuscript and fulfill all the requirements of every subheading. The sources used to gather data are the PubMed, Cochrane, and EMBASE databases, IEEE and Google and Google Scholar search engines. AMSTAR tool is the most appropriate for assessing the methodological quality of the included studies.

Results

The study exclusively included twenty articles, whereby seven focused on artificial intelligence in inguinal hernia surgery, six focused on abdominal hernia surgery, five on incisional hernia surgery, and two on AI in medical imaging and robotics in hernia surgery.

Conclusion

The outcomes of this study reveal a significant literature gap on artificial intelligence in hernia surgery. The results also indicate that studies focus on inguinal hernia surgery more than any other types of hernia surgery since the articles addressing the topic are more. The study implies that more research is necessary for the field to develop and enjoy the benefits associated with AI. Thus, this situation will allow the integration of AI in activities like medical imaging and surgeon training.