Real-Time Computer-Aided Detection of Colorectal Neoplasia During Colonoscopy : A Systematic Review and Meta-analysis.

BACKGROUND: Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps. PURPOSE: To quantify the benefits and harms of CADe in randomized trials. DESIGN: Systematic review and meta-analysis. (PROSPERO: CRD42022293181). DATA SOURCES: Medline, Embase, and Scopus databases through February 2023. STUDY SELECTION: Randomized trials comparing CADe-assisted with standard colonoscopy for polyp and cancer detection. DATA EXTRACTION: Adenoma detection rate (proportion of patients with ≥1 adenoma), number of adenomas detected per colonoscopy, advanced adenoma (≥10 mm with high-grade dysplasia and villous histology), number of serrated lesions per colonoscopy, and adenoma miss rate were extracted as benefit outcomes. Number of polypectomies for nonneoplastic lesions and withdrawal time were extracted as harm outcomes. For each outcome, studies were pooled using a random-effects model. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. DATA SYNTHESIS: Twenty-one randomized trials on 18 232 patients were included. The ADR was higher in the CADe group than in the standard colonoscopy group (44.0% vs. 35.9%; relative risk, 1.24 [95% CI, 1.16 to 1.33]; low-certainty evidence), corresponding to a 55% (risk ratio, 0.45 [CI, 0.35 to 0.58]) relative reduction in miss rate (moderate-certainty evidence). More nonneoplastic polyps were removed in the CADe than the standard group (0.52 vs. 0.34 per colonoscopy; mean difference [MD], 0.18 polypectomy [CI, 0.11 to 0.26 polypectomy]; low-certainty evidence). Mean inspection time increased only marginally with CADe (MD, 0.47 minute [CI, 0.23 to 0.72 minute]; moderate-certainty evidence). LIMITATIONS: This review focused on surrogates of patient-important outcomes. Most patients, however, may consider cancer incidence and cancer-related mortality important outcomes. The effect of CADe on such patient-important outcomes remains unclear. CONCLUSION: The use of CADe for polyp detection during colonoscopy results in increased detection of adenomas but not advanced adenomas and in higher rates of unnecessary removal of nonneoplastic polyps. PRIMARY FUNDING SOURCE: European Commission Horizon 2020 Marie Sklodowska-Curie Individual Fellowship.

Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials.

BACKGROUND AND AIMS: Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal. METHODS: We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method. RESULTS: A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]). CONCLUSIONS: The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.

Speedometer for withdrawal time monitoring during colonoscopy: a clinical implementation trial.

OBJECTIVES: Meticulous inspection of the mucosa during colonoscopy, represents a lengthier withdrawal time, but has been shown to increase adenoma detection rate (ADR). We investigated if artificial intelligence-aided speed monitoring can improve suboptimal withdrawal time. METHODS: We evaluated the implementation of a computer-aided speed monitoring device during colonoscopy at a large academic endoscopy center. After informed consent, patients ≥18 years undergoing colonoscopy between 5 March and 29 April 2021 were examined without the use of the speedometer, and with the speedometer between 29 April and 30 June 2021. All colonoscopies were recorded, and withdrawal time was assessed based on the recordings in a blinded fashion. We compared mean withdrawal time, percentage of withdrawal time ≥6 min, and ADR with and without the speedometer. RESULTS: One hundred sixty-six patients in each group were eligible for analyses. Mean withdrawal time was 9 min and 6.6 s (95% CI: 8 min and 34.8 s to 9 min and 39 s) without the use of the speedometer, and 9 min and 9 s (95% CI: 8 min and 45 s to 9 min and 33.6 s) with the speedometer; difference 2.3 s (95% CI: -42.3-37.7, p = 0.91). The ADRs were 45.2% (95% CI: 37.6-52.8) without the speedometer as compared to 45.8% (95% CI: 38.2-53.4) with the speedometer (p = 0.91). The proportion of colonoscopies with withdrawal time ≥6 min without the speedometer was 85.5% (95% CI: 80.2-90.9) versus 86.7% (95% CI: 81.6-91.9) with the speedometer (p = 0.75). CONCLUSIONS: Use of speed monitoring during withdrawal did not increase withdrawal time or ADR in colonoscopy. CLINICALTRIALS.GOV IDENTIFIER: NCT04710251.

Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy.

Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.

Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).

BACKGROUND & AIMS: Artificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps during colonoscopy. The effect of CADe during screening and surveillance colonoscopy has not previously been studied in a United States (U.S.) population. METHODS: We conducted a prospective, multi-center, single-blind randomized tandem colonoscopy study to evaluate a deep-learning based CADe system (EndoScreener, Shanghai Wision AI, China). Patients were enrolled across 4 U.S. academic medical centers from 2019 through 2020. Patients presenting for colorectal cancer screening or surveillance were randomized to CADe colonoscopy first or high-definition white light (HDWL) colonoscopy first, followed immediately by the other procedure in tandem fashion by the same endoscopist. The primary outcome was adenoma miss rate (AMR), and secondary outcomes included sessile serrated lesion (SSL) miss rate and adenomas per colonoscopy (APC). RESULTS: A total of 232 patients entered the study, with 116 patients randomized to undergo CADe colonoscopy first and 116 patients randomized to undergo HDWL colonoscopy first. After the exclusion of 9 patients, the study cohort included 223 patients. AMR was lower in the CADe-first group compared with the HDWL-first group (20.12% [34/169] vs 31.25% [45/144]; odds ratio [OR], 1.8048; 95% confidence interval [CI], 1.0780-3.0217; P = .0247). SSL miss rate was lower in the CADe-first group (7.14% [1/14]) vs the HDWL-first group (42.11% [8/19]; P = .0482). First-pass APC was higher in the CADe-first group (1.19 [standard deviation (SD), 2.03] vs 0.90 [SD, 1.55]; P = .0323). First-pass ADR was 50.44% in the CADe-first group and 43.64 % in the HDWL-first group (P = .3091). CONCLUSION: In this U.S. multicenter tandem colonoscopy randomized controlled trial, we demonstrate a decrease in AMR and SSL miss rate and an increase in first-pass APC with the use of a CADe-system when compared with HDWL colonoscopy alone.

Charting a path forward for clinical research in artificial intelligence and gastroenterology.

Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.

Adoption of New Technologies: Artificial Intelligence.

Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.

Preloaded 22-gauge fine-needle system facilitates placement of a higher number of fiducials for image-guided radiation therapy compared with traditional backloaded 19-gauge approach.

BACKGROUND AND AIMS: Image-guided radiation therapy (IGRT) often relies on EUS-guided fiducial markers. Previously used manually backloaded fiducial needles have multiple potential limitations including safety and efficiency concerns. Our aim was to evaluate the efficacy, feasibility, and safety of EUS-guided placement of gold fiducials using a novel preloaded 22-gauge needle compared with a traditional, backloaded 19-gauge needle. METHODS: This was a single-center comparative cohort study. Patients with pancreatic and hepatobiliary malignancy who underwent EUS-guided fiducial placement (EUS-FP) between October 2014 and February 2018 were included. The main outcome was the technical success of fiducial placement. Secondary outcomes were mean procedure time, fiducial visibility during IGRT, technical success of IGRT delivery, and adverse events. RESULTS: One hundred fourteen patients underwent EUS-FP during the study period. Of these, 111 patients had successful placement of a minimum of 2 fiducials. Fifty-six patients underwent placement using a backloaded 19-gauge needle and 58 patients underwent placement using a 22-gauge preloaded needle. The mean number of fiducials placed successfully at the target site was significantly higher in the 22-gauge group compared with the 19-gauge group (3.53 ± .96 vs 3.11 ± .61, respectively; P = .006). In the 22-gauge group, the clinical goal of placing 4 fiducials was achieved in 78%, compared with 23% in the 19-gauge group (P < .001). In univariate analyses, gender, age, procedure time, tumor size, and location did not influence the number of successfully placed fiducials. Technical success of IGRT with fiducial tracking was high in both the 19-gauge (51/56, 91%) and the 22-gauge group (47/58, 81%; P = .12). CONCLUSIONS: EUS-FP using a preloaded 22-gauge needle is feasible, effective, and safe and allows for a higher number of fiducials placed when compared with the traditional backloaded 19-gauge needle.

Benchmarking definitions of false-positive alerts during computer-aided polyp detection in colonoscopy.

BACKGROUND: The occurrence of false-positive alerts is an important outcome measure in computer-aided colon polyp detection (CADe) studies. However, there is no consensus definition of a false positive in clinical trials evaluating CADe in colonoscopy. We aimed to study the diagnostic performance of CADe based on different threshold definitions for false-positive alerts. METHODS: A previously validated CADe system was applied to screening/surveillance colonoscopy videos. Different thresholds for false-positive alerts were defined based on the time an alert box was continuously traced by the system. Primary outcomes were false-positive results and specificity using different threshold definitions of false positive. RESULTS: 62 colonoscopies were analyzed. CADe specificity and accuracy were 93.2 % and 97.8 %, respectively, for a threshold definition of ≥ 0.5 seconds, 98.6 % and 99.5 % for a threshold definition of ≥ 1 second, and 99.8 % and 99.9 % for a threshold definition of ≥ 2 seconds. CONCLUSION: Our analysis demonstrated how different threshold definitions of false positive can impact the reported diagnostic performance of CADe for colon polyp detection.

Training a computer-aided polyp detection system to detect sessile serrated adenomas using public domain colonoscopy videos.

Background Colorectal cancer (CRC) is a major public health burden worldwide, and colonoscopy is the most commonly used CRC screening tool. Still, there is variability in adenoma detection rate (ADR) among endoscopists. Recent studies have reported improved ADR using deep learning models trained on videos curated largely from private in-house datasets. Few have focused on the detection of sessile serrated adenomas (SSAs), which are the most challenging target clinically. Methods We identified 23 colonoscopy videos available in the public domain and for which pathology data were provided, totaling 390 minutes of footage. Expert endoscopists annotated segments of video with adenomatous polyps, from which we captured 509 polyp-positive and 6,875 polyp-free frames. Via data augmentation, we generated 15,270 adenomatous polyp-positive images, of which 2,310 were SSAs, and 20,625 polyp-negative images. We used the CNN AlexNet and fine-tuned its parameters using 90 % of the images, before testing its performance on the remaining 10 % of images unseen by the model. Results We trained the model on 32,305 images and tested performance on 3,590 images with the same proportion of SSA, non-SSA polyp-positive, and polyp-negative images. The overall accuracy of the model was 0.86, with a sensitivity of 0.73 and a specificity of 0.96. Positive predictive value was 0.93 and negative predictive value was 0.96. The area under the curve was 0.94. SSAs were detected in 93 % of SSA-positive images. Conclusions Using a relatively small set of publicly-available colonoscopy data, we obtained sizable training and validation sets of endoscopic images using data augmentation, and achieved an excellent performance in adenomatous polyp detection.

Regulatory considerations for artificial intelligence technologies in GI endoscopy.

Artificial intelligence (AI) technologies in clinical medicine have become the subject of intensive investigative efforts and popular attention. In domains ranging from pathology to radiology, AI has demonstrated the potential to improve clinical performance and efficiency. In gastroenterology, AI has been applied on multiple fronts, with particular progress seen in the areas of computer-aided polyp detection (CADe) and computer-aided polyp diagnosis (CADx), to assist gastroenterologists during colonoscopy. As clinical evidence accrues for CADe and CADx, our attention must also turn toward the unique challenges that this new wave of technologies represent for the U.S. Food and Drug Administration and other regulatory agencies, who are tasked with protecting public health by ensuring the safety of medical devices. In this review, we describe the current regulatory pathways for AI tools in gastroenterology and the expected evolution of these pathways.

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Computer-aided detection (CADe) systems, based on deep learning, might reduce rates of missed adenomas by displaying visual alerts that identify precancerous polyps on the endoscopy monitor in real time. We compared adenoma miss rates of CADe colonoscopy vs routine white-light colonoscopy. METHODS: We performed a prospective study of patients, 18-75 years old, referred for diagnostic, screening, or surveillance colonoscopies at a single endoscopy center of Sichuan Provincial People's Hospital from June 3, 2019 through September 24, 2019. Same day, tandem colonoscopies were performed for each participant by the same endoscopist. Patients were randomly assigned to groups that received either CADe colonoscopy (n=184) or routine colonoscopy (n=185) first, followed immediately by the other procedure. Endoscopists were blinded to the group each patient was assigned to until immediately before the start of each colonoscopy. Polyps that were missed by the CADe system but detected by endoscopists were classified as missed polyps. False polyps were those continuously traced by the CADe system but then determined not to be polyps by the endoscopists. The primary endpoint was adenoma miss rate, which was defined as the number of adenomas detected in the second-pass colonoscopy divided by the total number of adenomas detected in both passes. RESULTS: The adenoma miss rate was significantly lower with CADe colonoscopy (13.89%; 95% CI, 8.24%-19.54%) than with routine colonoscopy (40.00%; 95% CI, 31.23%-48.77%, P<.0001). The polyp miss rate was significantly lower with CADe colonoscopy (12.98%; 95% CI, 9.08%-16.88%) than with routine colonoscopy (45.90%; 95% CI, 39.65%-52.15%) (P<.0001). Adenoma miss rates in ascending, transverse, and descending colon were significantly lower with CADe colonoscopy than with routine colonoscopy (ascending colon 6.67% vs 39.13%; P=.0095; transverse colon 16.33% vs 45.16%; P=.0065; and descending colon 12.50% vs 40.91%, P=.0364). CONCLUSIONS: CADe colonoscopy reduced the overall miss rate of adenomas by endoscopists using white-light endoscopy. Routine use of CADe might reduce the incidence of interval colon cancers. chictr.org.cn study no: ChiCTR1900023086.

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system that avoids potential operational bias. METHODS: We did a double-blind randomised trial at the endoscopy centre in Caotang branch hospital of Sichuan Provincial People's Hospital in China. We enrolled consecutive patients (aged 18-75 years) presenting for diagnostic and screening colonoscopy. We excluded patients with a history of inflammatory bowel disease, colorectal cancer, or colorectal surgery or who had a contraindication for biopsy; we also excluded patients who had previously had an unsuccessful colonoscopy and who had a high suspicion for polyposis syndromes, inflammatory bowel disease, and colorectal cancer. We allocated patients (1:1) to colonoscopy with either the CADe system or a sham system. Randomisation was by computer-generated random number allocation. Patients and the endoscopist were unaware of the random assignment. To achieve masking, the output of the system was shown on a second monitor that was only visible to an observer who was responsible for reporting the alerts. The primary outcome was the adenoma detection rate (ADR), which is the proportion of individuals having a complete colonoscopy, from caecum to rectum, who had one or more adenomas detected. The primary analysis was per protocol. We also analysed characteristics of polyps and adenomas missed initially by endoscopists but detected by the CADe system. This trial is complete and is registered with http://www.chictr.org.cn, ChiCTR1800017675. FINDINGS: Between Sept 3, 2018, and Jan 11, 2019, 1046 patients were enrolled to the study, of whom 36 were excluded before randomisation, 508 were allocated colonoscopy with polyp detection using the CADe system, and 502 were allocated colonoscopy with the sham system. After further excluding patients who met exclusion criteria, 484 patients in the CADe group and 478 in the sham group were included in analyses. The ADR was significantly greater in the CADe group than in the sham group, with 165 (34%) of 484 patients allocated to the CADe system having one or more adenomas detected versus 132 (28%) of 478 allocated to the sham system (odds ratio 1·36, 95% CI 1·03-1·79; p=0·030). No complications were reported among all colonoscopy procedures. Polyps initially missed by the endoscopist but identified by the CADe system were generally small in size, isochromatic, flat in shape, had an unclear boundary, were partly behind colon folds, and were on the edge of the visual field. INTERPRETATION: Polyps initially missed by the endoscopist had characteristics that are sometimes difficult for skilled endoscopists to recognise. Such polyps could be detected using a high-performance CADe system during colonoscopy. The effect of CADe during colonoscopy on the incidence of interval colorectal cancer should be investigated. FUNDING: None.

The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study.

BACKGROUND: Computer-aided detection (CADe) of colon polyps has been demonstrated to improve colon polyp and adenoma detection during colonoscopy by indicating the location of a given polyp on a parallel monitor. The aim of this study was to investigate whether embedding the CADe system into the primary colonoscopy monitor may serve to increase polyp and adenoma detection, without increasing physician fatigue level. METHODS: Consecutive patients presenting for colonoscopies were prospectively randomized to undergo routine colonoscopy with or without the assistance of a real-time polyp detection CADe system. Fatigue level was evaluated from score 0 to 10 by the performing endoscopists after each colonoscopy procedure. The main outcome was adenoma detection rate (ADR). RESULTS: Out of 790 patients analyzed, 397 were randomized to routine colonoscopy (control group), and 393 to a colonoscopy with computer-aided diagnosis (CADe group). The ADRs were 20.91% and 29.01%, respectively (OR = 1.546, 95% CI 1.116-2.141, p = 0.009). The average number of adenomas per colonoscopy (APC) was 0.29 and 0.48, respectively (Change Folds = 1.64, 95% CI 1.299-2.063, p < 0.001). The improvement in polyp detection was mainly due to increased detection of non-advanced diminutive adenomas, serrated adenoma and hyperplastic polyps. The fatigue score for each procedure was 3.28 versus 3.40 for routine and CADe group, p = 0.357. CONCLUSIONS: A real-time CADe system employed on the primary endoscopy monitor may lead to improvements in ADR and polyp detection rate without increasing fatigue level during colonoscopy. The integration of a low-latency and high-performance CADe systems may serve as an effective quality assurance tool during colonoscopy. www.chictr.org.cn number, ChiCTR1800018058.

Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.

The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learning algorithm can detect polyps in clinical colonoscopies, in real time and with high sensitivity and specificity. We developed the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitivity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sensitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80 ± 5.60 ms in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in polyp and adenoma detection performance among endoscopists.

Chronic Abdominal Wall Pain: An Under-Recognized Diagnosis Leading to Unnecessary Testing.

Chronic abdominal wall pain (CAWP) refers to a condition wherein pain originates from the abdominal wall itself rather than the underlying viscera. According to various estimates, 10% to 30% of patients with chronic abdominal pain are eventually diagnosed with CAWP, usually after expensive testing has failed to uncover another etiology. The most common cause of CAWP is anterior cutaneous nerve entrapment syndrome. The diagnosis of CAWP is made using an oft-forgotten physical examination finding known as Carnett's sign, where focal abdominal tenderness is either the same or worsened during contraction of the abdominal musculature. CAWP can be confirmed by response to trigger point injection of local anesthetic. Once diagnosis is made, treatment ranges from conservative management to trigger point injection and in refractory cases, even surgery. This review provides an overview of CAWP, discusses the cost and implications of a missed diagnosis, compares somatic versus visceral innervation, describes the pathophysiology of nerve entrapment, and reviews the evidence behind available treatment modalities.