Introduction

Clostridioides difficile infection (CDI) is the most common cause of nosocomial diarrhoea, and
during the last decades its incidence has significantly increased, not only in the hospital setting but
also in the community (1). CDI includes severe clinical pictures of disease. Fecal microbiota
transplantation (FMT) is an established treatment for recurrent CDI, but its role in severe clinical
pictures of CDI is still not well defined.

Aims & Methods

Our aim is to report efficacy and safety of FMT in a large cohort of patients with severe and severe-
complicated CDI. We included patients with severe CDI treated with FMT at our center from June
2013 to August 2024. Severe and severe-complicated CDI were defined according to international
guidelines. Data was collected on patient demographics, CDI characteristics, including disease
severity, risk factors and concomitant diseases. Enrolled patients were followed up at 12 weeks after
the last FMT. The primary outcome was the clinical resolution of CDI at 8 weeks after FMT, while
the secondary outcomes were the overall survival at 90 days and the safety of FMT. Donor stools
were screened following international guidelines. Prior to fecal infusion, all patients underwent a 3-
day antibiotic pre-treatment regimen with oral vancomycin or fidaxomicin, as well as bowel
cleansing. FMT procedures were carried out by colonoscopy using a single-donor solution of at
least 50 grams of fresh or frozen feces. A sequential FMT approach was planned in patients with
pseudomembranous colitis (PMC), until the disappearance of pseudomembranes, or in patients who
experienced diarrhea after the initial FMT with a persistence of C. difficile toxin on stool samples.

Results

125 patients were enrolled (60.8% female, mean age 71 years). One-hundred and ten patients had
severe CDI (88%), 15 had severe complicated CDI (12%). Of them, 73 patients had
pseudomembranous colitis (58.4%). The median number of CDI recurrences was 2. A sequential
FMT protocol was performed in 62 patients (49.6%), for a total of 235 FMT procedures. Regarding
risk factors, the most frequent were hospitalization (107 patients – 85.6%), antibiotic use in the 30
days prior to infection (94 patients – 75.2%), chronic therapy with proton pump inhibitors (74
patients – 59.2%) and enticement (39 patients – 31.2%). The median Charlson Comorbidity Index
(CCI) was 5. Overall, 108 patients (86.4%) experienced resolution of CDI (97 with severe CDI and
11 with severe-complicated CDI). 18 patients (14.4%) died within the follow-up period. No deaths
were attributable to FMT. Severe adverse events occurred in 11 (8.8%) patients and 6 were
potentially associated with FMT and/or failure to cure CDI.

Conclusion

In our cohort of patients, FMT was a highly effective and safe treatment for severe and severe-
complicated CDI.

References

1 - Liu C, Monaghan T, Yadegar A, Louie T, Kao D. Insights into the Evolving Epidemiology of
Clostridioides difficile Infection and Treatment: A Global Perspective. Antibiotics (Basel). 2023 Jul
1;12(7).

Introduction

While anatomical landmark identification and adequate inspection time are recognized as crucial quality indicators in upper endoscopy, they are difficult to document consistently and accurately based solely on endoscopist reports.

Aims & Methods

To address this issue at Assuta Healthcare, a multicenter organization with 20 endoscopy units and over 200 endoscopists, we implemented a video-based AI platform designed to measure total upper endoscopy time (EGD-T), assess photo documentation of landmarks, and streamline the photo labeling process. In the current study, we evaluated the platform and employed it to examine endoscopist-reported quality measures.
For all upper endoscopies performed at Assuta Healthcare in 2024 on patients 45 years and older, the Theator AI-based Surgical Intelligence platform automatically documented total procedure time as well as photo snapshots captured and labeled by endoscopists for seven landmarks: 1. esophagus, 2. gastroesophageal junction, 3. cardia or fundus, 4. body, 5. angulus, 6. antrum or pylorus, and 7. duodenum (second part). In a subset of procedures, the platform facilitated documentation by suggesting labels for captured snapshots. After validating platform accuracy, we examined platform-reported procedure times. We then assessed landmark photo documentation rates in procedures with and without platform labeling suggestions.

Results

The study included 21,989 upper endoscopies (60.7% female, median age of 62.2 years), of which 51.7% were conducted with platform labeling suggestions and the remaining 48.3% were conducted without. Validated platform accuracy was 98%. Median total procedure time measured by the platform was 3.18 minutes (IQR 2.18-4.25). When they did not receive suggestions, endoscopists did not label any landmarks in 25% of procedures. At 12%, this percentage was significantly lower (p < .001) when the platform did provided suggestions. This difference was also reflected in the median number of landmarks labeled, which was 2 (IQR 0-3) in procedures without labeling suggestions and 3 (IQR 1-4) in procedures with labeling suggestions (p < .001).

Conclusion

Implementing an AI tool to automatically assess procedure time in upper endoscopy provided accurate measurements, revealing durations considerably lower than recommended. It further revealed extremely low labeling rates, which increased when automated suggestions were provided. The results highlight critical gaps in the reporting of quality measures for upper endoscopy and indicate that AI-based documentation can be instrumental to quality assurance by identifying and addressing such gaps.

Disclosure

None

Introduction

To effectively fulfill their function as key quality indicators in colonoscopy, documented withdrawal times and labeled snapshots of anatomical landmarks must be accurate. However, assessing landmark documentation and labeling is challenging, and current quality assurance programs rely on endoscopist reports rather than objective measures of Colonoscopy Withdrawal Time (CWT).

Aims & Methods

We aimed to evaluate a video-based AI platform designed to measure CWT, assess photo documentation of landmarks, and streamline the photo labeling process. The platform was implemented as part of the quality assurance program at Assuta Healthcare, a multicenter organization with 20 endoscopy units and over 200 endoscopists.
The study included all colonoscopies performed at Assuta Healthcare in 2024 on patients 50 years and older, excluding patients with previous or current diagnosis of inflammatory bowel disease and procedures that were not completed due to inadequate preparation. For all included procedures, the Theator AI-based Surgical Intelligence platform automatically documented photo snapshots (cecum and rectum) and associated labels, as captured and entered by endoscopists. In a subset of procedures, it further facilitated documentation during endoscopy by suggesting labels for captured snapshots. For procedures with photo documentation of the cecum, the platform measured CWT. After validating platform accuracy, we assessed labeled photo documentation rates in procedures with and without platform labeling suggestions. We compared platform CWTs to endoscopist-reported CWTs in all procedures and then separately for those with and without at least one detected polyp, noting the proportion of procedures meeting the duration criterion of 6 minutes.

Results

The study included 49,545 colonoscopies (51.5% female; median age 62.2 years), with an overall polyp detection rate of 42.3%. Validated platform accuracy was 98%. The cecum was captured and labeled in 86.1% of procedures overall, with a significantly higher (p < .001) capture rate (91%; 23,279 of 25,565) in procedures with platform label suggestions than in those without (86.9%; 19,794 of 23,980). A similar pattern was shown for rectum snapshots, which were captured and labeled in 60% of procedures overall, with a significantly higher (p < .001) capture rate (69%; 16,759 of 24,244) in procedures with platform label suggestions than in those without (51.9%; 13,137 of 25,304). In the 43,073 procedures with cecum labels, median AI-measured CWT versus endoscopist-reported CWT was 317 seconds (IQR 211-419) versus 780 seconds (IQR 600-1,020) for procedures without a polyp, and 489 seconds (IQR 358-676) versus 1,020 seconds (IQR 780-1,380) for procedures with a polyp (p < .001 for both). These differences were reflected in the proportion of procedures with CWTs under 6 minutes as reported by the platform versus the endoscopist, with 60.4% versus 40.1% in procedures without a polyp and 25.1% versus 15.4% with a polyp (p < .001 for both).

Conclusion

Implementation of an AI tool to automatically assess CWT and document anatomical landmarks provided accurate measurements, with automated suggestions facilitating labeling. Furthermore, it revealed that reported CWT times were significantly longer than measured times, frequently indicating that the 6-minute minimum duration criterion was achieved when it was not. This issue is critical for evaluating and achieving quality in colonoscopy and should be addressed as part of improvement efforts.

Disclosure

None

Introduction

Artificial Intelligence (AI) assisted reading in Small Bowel Capsule Endoscopy (SBCE) has recently been shown to achieve comparable and potentially superior accuracy compared to standard clinician reading. In Colon Capsule Endoscopy (CCE), AI algorithms have also demonstrated promising results. However, the extent of AI-assisted reading's advantage remains unclear, particularly regarding its performance across different polyp sizes, morphologies, locations, and other factors. A deeper understanding of these variables is crucial for optimising AI performance and facilitating its clinical integration.

Aims & Methods

This CESCAIL sub-analysis evaluates the per-polyp diagnostic accuracy of AI-assisted versus standard clinician assessment (readings) and identifies key factors influencing AI-assisted accuracy using AiSPEED^TM, the Computer-Aided Detection (CADe) system.
The CESCAIL study evaluated the diagnostic accuracy of AI-assisted CCE readings using AiSPEED™ compared to the standard readings. Standard readings were manually performed by clinicians, while AI-assisted readings incorporated an AI pre-review followed by clinician validation. A total of 1,803 polyps from 673 patients were analysed at the per-polyp level, focusing on sensitivity and positive predictive value. Factors influencing the improved accuracy of AI-assisted readings over standard clinician readings were also assessed, including polyp size, morphology, location, patient demographics (age and sex), bowel preparation quality, capsule excretion rates, comorbidities, medications, reading time, and video duration.

Results

AI-assisted reading demonstrated significantly higher sensitivity with clear superiority for smaller polyps (<10 mm) compared to larger ones (≥10 mm) (OR 2.27 vs 0.88, p<0.001) (see Table 1). While there was no observed difference in diagnostic accuracy between AI-assisted and standard clinician assessment for polyps ≥10 mm, non-inferiority was established. AI accuracy remained consistent between 6–9 mm and ≤5 mm polyps (p=0.64). The most notable improvement was observed with hyperplastic polyps (HPs) (OR 5.4, p<0.001). No significant differences were identified for pedunculated, sub-pedunculated, Lateral Spreading Tumours, or Sessile Serrated Lesions. Furthermore, AI-assisted readings were significantly more accurate for left-sided polyps than right-sided ones (OR 2.36 vs 1.66, p<0.0001), although AI-assisted reads outperformed standard reads in both locations.

Conclusion

This study highlights the performance and consistency of AI-assisted reading, particularly for detecting smaller adenomas and HPs, with notably improved accuracy in the left colon. Next-generation AI should focus on distinguishing significant from diminutive polyps and enhancing polyp characterisation, especially for right-sided lesions.

References

1. Spada C, Piccirelli S, Hassan C, Ferrari C, Toth E, Gonzalez-Suarez B, et al. AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study. Lancet Digit Health. 2024;6(5):e345-e53.

Disclosure

Hagen Wenzek serves as the CEO, Elizabeth White is the Project Manager, and Pablo Laiz is the AI software engineer at Corporate Health International, which owns the intellectual properties of AiSPEED^TM.

Introduction

The diagnosis of small bowel motility disorders currently relies on identifying abnormal contractile patterns through intestinal manometry, but the technique is restricted to referral centers. Conventional CT scanning, frequently performed in the routine evaluation of these patients, provides images of intestinal content, which might serve as a marker of intestinal dysfunction. We hypothesized that pooling of intestinal content, quantified through computerized analysis of abdominal CT images, could serve as an indirect marker of small intestinal motor dysfunction.

Aims & Methods

Consecutive patients with either a) chronic intestinal pseudo-obstruction (CIPO) or b) suspected small intestinal motor dysfunction, evaluated at our Motility Unit using high-resolution jejunal manometry, were prospectively included. Manometric tracings were categorized as
a) normal,
b) uncoordinated contractile activity with preserved amplitude, or
c) hypomotility (markedly diminished amplitude of contractile activity).
All patients also underwent an abdominal CT scan within three months of the manometric study, as part of the clinical evaluation of the motility disorder. CT images were analyzed using a validated software developed in our laboratory, which applies image processing techniques and deep-learning tools to identify and quantify intestinal content volumes in the small bowel and colon of:
a) gas,
b) chyme-type content (non-gaseous, homogeneous texture), and
c) fecal-type content (non-gaseous, heterogeneous texture).

Results

Of the 66 patients included, 9 had CIPO and 57 suspected dysmotility. Manometry was abnormal in all patients with CIPO and in 22 patients (39%) without CIPO. Total intestinal content was markedly increased in patients with CIPO as compared with patients with normal motility (3034±826 mL vs 398±43 mL, respectively; p<0.001), primarily due to an increase in gas (1086±325 mL vs 22±4; p<0.001) and chyme-type content in the small bowel (1282±544 mL vs 81±11; p<0.001).
Among patients without CIPO, the volume of small bowel content was increased in patients with dysmotility compared to those with normal motility, mainly of chyme-type content (191±39 vs 81±11 mL in normal motility; p=0.006). Colonic volumes of gas and fecal-type content were also higher in patients with dysmotility than in those with normal motility (gas 224±40 vs 115±18 mL, p=0.027; fecal-type 244±59 vs 115±19 mL, p=0.036). Dysmotility patients with hypomotility patterns had overall higher intestinal content volumes than patients with uncoordinated contractile patterns (Table 1).

Table 1. Intestinal content volumes in non-CIPO patients, classified by manometric patterns

Conclusion

Intestinal content, measured on conventional CT scans by a specific analysis program, provides a useful indication of intestinal dysmotility.