Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry

Bionic concepts for assessment of defecatory function and dysfunction

Bionic technology is gaining momentum in medical research. In gastroenterology, bionic technologies such as the PillCam and SmartPill assess intestinal mucosa morphology and function during the gastrointestinal passage of the devices. Oral drug delivery devices and intestinal robots are other bionic technologies under development. Recently, Fecobionics, a simulated feces, was developed for assessment of anorectal (defecatory) function. It is an anally insertable device with shape and consistency like feces. The integrated device measures anorectal pressures, orientation, bending (a proxy of the anorectal angle), and the shape of the device when located in rectum and when being evacuated by patients. It integrates most elements of the current technologies on the market (balloon expulsion technology, high-resolution anorectal manometry, defecography, and the functional luminal imaging probe). Multiple measurements in a single study by a bionic device have obvious advantages since novel functional parameters can be computed. Several Fecobionics prototypes have been developed and evaluated in normal human subjects and in patients with fecal incontinence and defecatory disorders such as obstructed defecation. This paper provides an overview of the Fecobionics platform for assessment of defecatory function and dysfunction with a focus on design, signal processing, data analysis, current clinical trials, and future applications in diagnostics, therapy assessment, and therapy.

Artificial intelligence as a transforming factor in motility disorders-automatic detection of motility patterns in high-resolution anorectal manometry

High-resolution anorectal manometry (HR-ARM) is the gold standard for anorectal functional disorders' evaluation, despite being limited by its accessibility and complex data analysis. The London Protocol and Classification were developed to standardize anorectal motility patterns classification. This proof-of-concept study aims to develop and validate an artificial intelligence model for identification and differentiation of disorders of anal tone and contractility in HR-ARM. A dataset of 701 HR-ARM exams from a tertiary center, classified according to London Classification, was used to develop and test multiple machine learning (ML) algorithms. The exams were divided in a training and testing dataset with a 80/20% ratio. The testing dataset was used for models' evaluation through its accuracy, sensitivity, specificity, positive and negative predictive values and area under the receiving-operating characteristic curve. LGBM Classifier had the best performance, with an accuracy of 87.0% for identifying disorders of anal tone and contractility. Different ML models excelled in distinguishing specific disorders of anal tone and contractility, with accuracy over 90.0%. This is the first worldwide study proving the accuracy of a ML model for differentiation of motility patterns in HR-ARM, demonstrating the value of artificial intelligence models in optimizing HR-ARM availability while reducing interobserver variability and increasing accuracy.

Feasibility of IAPWG protocol in performing high-definition three-dimensional anorectal manometry: A prospective, multicentric italian study

BACKGROUND

The International Anorectal Physiology Working Group (IAPWG) suggests a standardized protocol to perform high-resolution anorectal manometry. The applicability and possible limitations of the IAPWG protocol in performing three-dimensional high-definition anorectal manometry (3D-ARM) have still to be extensively evaluated.

METHODS

The IAPWG protocol was applied in performing 3D-ARM. Anorectal manometry (ARM) and a balloon expulsion test (BET) were performed according to IAPGW protocol in 290 patients.

KEY RESULTS

A total of 84 males and 206 females (mean age 57.1 ± 15.7 years) were enrolled in six Italian centers. The reasons for which the patients were sent to perform 3D-ARM were: constipation (53.1%), fecal incontinence (26.9%), anal pain (3.1%), postsurgical (3.8%) and presurgical evaluation (4.8%), prolapse (3.4%), anal fissure (2.8%), and other (2.1%). Due to organic and functional conditions (low rectal anterior resections, rectal prolapses, and J-pouch after colectomy), we were unable to perform a complete 3D-ARM on six patients. Overall, a complete 3D-ARM and BET following IAPWG protocol was carried out in 284 patients (97.9%). The following were recorded: rest pressure (81.9 ± 32.0 mmHg) and length of the anal sphincter (37.0 ± 6.2 cm), maximum anal squeeze pressure (201.6 ± 81.3 mmHg), squeeze duration (22.0 ± 8.8 s), maximum rectal (48.7 ± 41.0 mmHg) and minimum anal pressure (73.3 ± 36.5 mmHg) during push, presence/absence of a dyssynergic pattern, cough reflex and rectal sensations (first constant sensation 48.4 ± 29.5 mL, desire to defecate 83.7 ± 52.1 mL, and maximum tolerated volume 149.5 ± 72.6 mL), and presence/absence of rectoanal inhibitory reflex. Mean 3D-ARM registration time was 14 min 7 s ± 3 min 12 s.

CONCLUSIONS

This is the first multicentric study that evaluates the applicability of the IAPWG protocol in 3D-ARM performed in different manometric laboratories (both gastroenterological and surgical). The IAPWG protocol was easy to perform and was not time consuming. A diagnosis according to the London Classification was easily obtained in most patients in which 3D-ARM was carried out. No clear limitations to the applicability of the IAPWG protocol were detected.

Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications

Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.

Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility

PURPOSE OF REVIEW

Artificial intelligence (AI) is a broad term that pertains to a computer's ability to mimic and sometimes surpass human intelligence in interpretation of large datasets. The adoption of AI in gastrointestinal motility has been slower compared to other areas such as polyp detection and interpretation of histopathology.

RECENT FINDINGS

Within esophageal physiologic testing, AI can automate interpretation of image-based tests, especially high resolution manometry (HRM) and functional luminal imaging probe (FLIP) studies. Basic tasks such as identification of landmarks, determining adequacy of the HRM study and identification from achalasia from non-achalasia patterns are achieved with good accuracy. However, existing AI systems compare AI interpretation to expert analysis rather than to clinical outcome from management based on AI diagnosis. The use of AI methods is much less advanced within the field of ambulatory reflux monitoring, where challenges exist in assimilation of data from multiple impedance and pH channels. There remains potential for replication of the AI successes within esophageal physiologic testing to HRM of the anorectum, and to innovative and novel methods of evaluating gastric electrical activity and motor function. The use of AI has tremendous potential to improve detection of dysmotility within the esophagus using esophageal physiologic testing, as well as in other regions of the gastrointestinal tract. Eventually, integration of patient presentation, demographics and alternate test results to individual motility test interpretation will improve diagnostic precision and prognostication using AI tools.