Does the PillCam SB3 capsule endoscopy system improve image reading efficiency irrespective of experience? A pilot study

Vision transformer distillation for enhanced gastrointestinal abnormality recognition in wireless capsule endoscopy images

PURPOSE

Wireless capsule endoscopy (WCE) is a non-invasive technology used for diagnosing gastrointestinal abnormalities. A single examination generates ∼ 55,000 images, making manual review both time-consuming and costly for doctors. Therefore, the development of computer vision-assisted systems is highly desirable to aid in the diagnostic process.

APPROACH

We presents a deep learning approach leveraging knowledge distillation (KD) from a convolutional neural network (CNN) teacher model to a vision transformer (ViT) student model for gastrointestinal abnormality recognition. The CNN teacher model utilizes attention mechanisms and depth-wise separable convolutions to extract features from WCE images, supervising the ViT in learning these representations.

RESULTS

The proposed method achieves accuracy of 97% and 96% on the Kvasir and KID datasets, respectively, demonstrating its effectiveness in distinguishing normal from abnormal regions and bleeding from non-bleeding cases. The proposed approach offers computational efficiency and generalization to unseen datasets, outperforming several state-of-the-art methods.

CONCLUSIONS

We proposed a deep learning approach utilizing CNNs and a ViT with KD to effectively classify gastrointestinal diseases in WCE images. It demonstrates promising performance on public datasets, distinguishing normal from abnormal regions and bleeding from non-bleeding cases while offering optimal computational efficiency compared with existing methods, making it suitable for GI disease applications.

UViT-Seg: An Efficient ViT and U-Net-Based Framework for Accurate Colorectal Polyp Segmentation in Colonoscopy and WCE Images

Colorectal cancer (CRC) stands out as one of the most prevalent global cancers. The accurate localization of colorectal polyps in endoscopy images is pivotal for timely detection and removal, contributing significantly to CRC prevention. The manual analysis of images generated by gastrointestinal screening technologies poses a tedious task for doctors. Therefore, computer vision-assisted cancer detection could serve as an efficient tool for polyp segmentation. Numerous efforts have been dedicated to automating polyp localization, with the majority of studies relying on convolutional neural networks (CNNs) to learn features from polyp images. Despite their success in polyp segmentation tasks, CNNs exhibit significant limitations in precisely determining polyp location and shape due to their sole reliance on learning local features from images. While gastrointestinal images manifest significant variation in their features, encompassing both high- and low-level ones, a framework that combines the ability to learn both features of polyps is desired. This paper introduces UViT-Seg, a framework designed for polyp segmentation in gastrointestinal images. Operating on an encoder-decoder architecture, UViT-Seg employs two distinct feature extraction methods. A vision transformer in the encoder section captures long-range semantic information, while a CNN module, integrating squeeze-excitation and dual attention mechanisms, captures low-level features, focusing on critical image regions. Experimental evaluations conducted on five public datasets, including CVC clinic, ColonDB, Kvasir-SEG, ETIS LaribDB, and Kvasir Capsule-SEG, demonstrate UViT-Seg's effectiveness in polyp localization. To confirm its generalization performance, the model is tested on datasets not used in training. Benchmarking against common segmentation methods and state-of-the-art polyp segmentation approaches, the proposed model yields promising results. For instance, it achieves a mean Dice coefficient of 0.915 and a mean intersection over union of 0.902 on the CVC Colon dataset. Furthermore, UViT-Seg has the advantage of being efficient, requiring fewer computational resources for both training and testing. This feature positions it as an optimal choice for real-world deployment scenarios.

Robotic Pills as Innovative Personalized Medicine Tools: A Mini Review

The most common route for drug administration is the oral route due to the various advantages offered by this route, such as ease of administration, controlled and sustained drug delivery, convenience, and non-invasiveness. In spite of this, oral drug absorption faces challenges due to various issues related to its stability, permeability and solubility in the GI tract. Biologic drugs generally face problems when administered by oral route as they are readily degradable and thus required to be injected. To overcome these issues in oral absorption, different approaches like novel drug delivery systems and newer pharmaceutical technologies have been adopted. With a combined knowledge of drug delivery and pharmaceutical technology, robotic pills can be designed and used successfully to enhance the adhesion and permeation of drugs through the mucus membrane of the GI tract to achieve drug delivery at the target site. The potential application of robotic pills in diagnosis and drug dispensing is also discussed. The review highlights recent developments in robotic pill drug-device technology and discusses its potential applications to solve the problems and challenges in oral drug delivery.

Disease surveillance evaluation of primary small-bowel follicular lymphoma using capsule endoscopy images based on a deep convolutional neural network (with video)

BACKGROUND AND AIMS

Capsule endoscopy (CE) is useful in evaluating disease surveillance for primary small-bowel follicular lymphoma (FL), but some cases are difficult to evaluate objectively. This study evaluated the usefulness of a deep convolutional neural network (CNN) system using CE images for disease surveillance of primary small-bowel FL.

METHODS

We enrolled 26 consecutive patients with primary small-bowel FL diagnosed between January 2011 and January 2021 who underwent CE before and after a watch-and-wait strategy or chemotherapy. Disease surveillance by the CNN system was evaluated by the percentage of FL-detected images among all CE images of the small-bowel mucosa.

RESULTS

Eighteen cases (69%) were managed with a watch-and-wait approach, and 8 cases (31%) were treated with chemotherapy. Among the 18 cases managed with the watch-and-wait approach, the outcome of lesion evaluation by the CNN system was almost the same in 13 cases (72%), aggravation in 4 (22%), and improvement in 1 (6%). Among the 8 cases treated with chemotherapy, the outcome of lesion evaluation by the CNN system was improvement in 5 cases (63%), almost the same in 2 (25%), and aggravation in 1 (12%). The physician and CNN system reported similar results regarding disease surveillance evaluation in 23 of 26 cases (88%), whereas a discrepancy between the 2 was found in the remaining 3 cases (12%), attributed to poor small-bowel cleansing level.

CONCLUSIONS

Disease surveillance evaluation of primary small-bowel FL using CE images by the developed CNN system was useful under the condition of excellent small-bowel cleansing level.

Small Bowel Capsule Endoscopy: Experience from a single large tertiary care centre

Background and study aims Capsule endoscopy (CE) has transformed examination of the small bowel (SB), once considered a dark continent. The present study aimed to describe the indications, diagnostic yield, practical issues and complications of CE in one of the largest tertiary center in India. Patients and methods This retrospective analysis from a prospectively maintained database, conducted from January 2013 to June 2021 included 1155 CEs performed during this period. Patient medical records were reviewed for indications, results, and complications of CE. Results A total of 1154 patients (809 males and 345 females), mean age 53 years (range 6-87 years), one capsule got stuck in the esophagus, were included in the study. Active SB bleeding had no effect on SB transit time (324.7±161 minutes, n = 137 patients with active bleed vs 310.6±166.9 minutes, n = 1017 patients without active bleed; P = 0.35). The indication and diagnostic yield (DY) of CE were potential overt SB bleed (68.6% & 43.9%), potential occult SB bleed (8.2% and 40%), chronic diarrhea (7.9% and 28.4%), abdominal pain (6.5% and 21.3%), anemia (5.9% and 57.9%), and suspected/known case of Crohn's disease (2.3% & 56.5%) respectively. The DY for patients with age ≥60 years was similar to those with age < 60 years (61.9% vs. 51.8% respectively; P = 0.4). 21 patients (1.8%) had capsule retention of which six (0.5%) had to be referred for surgery. Conclusions CE is a safe and effective investigation with ever increasing range of indications. Potential SB bleed remains the most common indication for CE with high detection rate.

Video Capsule Endoscopy in Gastroenterology

Video capsule endoscopy (VCE) is a wireless technology used by gastroenterologists for various indications in their clinical practice. There has been significant improvement in this technology since its start about two decades ago. Specific video capsules have been made to evaluate the small bowel, colon, and esophagus. Now pan-enteric video capsule is available to assess both the small bowel and colon. VCE is a non-invasive procedure that has been tremendously evaluated for various gastrointestinal disorders, particularly small intestinal bleeding. There are specific contraindications and complications of VCE. This procedure has the technical part and video reading part. Newer software programs will come to reduce the reading time. Artificial intelligence is also coming for quick and accurate diagnosis of any positive findings during VCE. VCE is an important diagnostic test in the field of gastroenterology. Although it is an addition to optical endoscopic procedures to visualize the gastrointestinal mucosa, it has advantages and disadvantages.

Capsule Endoscopy: Pitfalls and Approaches to Overcome

Capsule endoscopy of the gastrointestinal tract is an innovative technology that serves to replace conventional endoscopy. Wireless capsule endoscopy, which is mainly used for small bowel examination, has recently been used to examine the entire gastrointestinal tract. This method is promising for its usefulness and development potential and enhances convenience by reducing the side effects and discomfort that may occur during conventional endoscopy. However, capsule endoscopy has fundamental limitations, including passive movement via bowel peristalsis and space restriction. This article reviews the current scientific aspects of capsule endoscopy and discusses the pitfalls and approaches to overcome its limitations. This review includes the latest research results on the role and potential of capsule endoscopy as a non-invasive diagnostic and therapeutic device.

Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

INTRODUCTION

Since the advent of artificial intelligence (AI) in clinical studies, luminal gastrointestinal endoscopy has made great progress, especially in the detection and characterization of neoplastic and preneoplastic lesions. Several studies have recently shown the potential of AI-driven endoscopy for the investigation of inflammatory bowel disease (IBD). This systematic review provides an overview of the current position and future potential of AI in IBD endoscopy.

METHODS

A systematic search was carried out in PubMed and Scopus up to 2 December 2020 using the following search terms: artificial intelligence, machine learning, computer-aided, inflammatory bowel disease, ulcerative colitis (UC), Crohn's disease (CD). All studies on human digestive endoscopy were included. A qualitative analysis and a narrative description were performed for each selected record according to the Joanna Briggs Institute methodologies and the PRISMA statement.

RESULTS

Of 398 identified records, 18 were ultimately included. Two-thirds of these (12/18) were published in 2020 and most were cross-sectional studies (15/18). No relevant bias at the study level was reported, although the risk of publication bias across studies cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture. Most studies focused on capsule endoscopy readings in CD (n = 5) and on the AI-assisted assessment of mucosal activity in UC (n = 10) for automated endoscopic scoring or real-time prediction of histological disease.

DISCUSSION

AI-assisted endoscopy in IBD is a rapidly evolving research field with promising technical results and additional benefits when tested in an experimental clinical scenario. External validation studies being conducted in large and prospective cohorts in real-life clinical scenarios will help confirm the added value of AI in assessing UC mucosal activity and in CD capsule reading.

PLAIN LANGUAGE SUMMARY

Artificial intelligence for inflammatory bowel disease endoscopy Artificial intelligence (AI) is a promising technology in many areas of medicine. In recent years, AI-assisted endoscopy has been introduced into several research fields, including inflammatory bowel disease (IBD) endoscopy, with promising applications that have the potential to revolutionize clinical practice and gastrointestinal endoscopy.We have performed the first systematic review of AI and its application in the field of IBD and endoscopy.A formal process of paper selection and analysis resulted in the assessment of 18 records. Most of these (12/18) were published in 2020 and were cross-sectional studies (15/18). No relevant biases were reported. All studies showed positive results concerning the novel technology evaluated, so the risk of publication bias cannot be ruled out at this early stage.Eleven records dealt with UC, five with CD and two with both. Most studies focused on capsule endoscopy reading in CD patients (n = 5) and on AI-assisted assessment of mucosal activity in UC patients (n = 10) for automated endoscopic scoring and real-time prediction of histological disease.We found that AI-assisted endoscopy in IBD is a rapidly growing research field. All studies indicated promising technical results. When tested in an experimental clinical scenario, AI-assisted endoscopy showed it could potentially improve the management of patients with IBD.Confirmatory evidence from real-life clinical scenarios should be obtained to verify the added value of AI-assisted IBD endoscopy in assessing UC mucosal activity and in CD capsule reading.

Video Capsule Endoscopy and Ingestible Electronics: Emerging Trends in Sensors, Circuits, Materials, Telemetry, Optics, and Rapid Reading Software

Real-time monitoring of the gastrointestinal tract in a safe and comfortable manner is valuable for the diagnosis and therapy of many diseases. Within this realm, our review captures the trends in ingestible capsule systems with a focus on hardware and software technologies used for capsule endoscopy and remote patient monitoring. We introduce the structure and functions of the gastrointestinal tract, and the FDA guidelines for ingestible wireless telemetric medical devices. We survey the advanced features incorporated in ingestible capsule systems, such as microrobotics, closed-loop feedback, physiological sensing, nerve stimulation, sampling and delivery, panoramic imaging with adaptive frame rates, and rapid reading software. Examples of experimental and commercialized capsule systems are presented with descriptions of their sensors, devices, and circuits for gastrointestinal health monitoring. We also show the recent research in biocompatible materials and batteries, edible electronics, and alternative energy sources for ingestible capsule systems. The results from clinical studies are discussed for the assessment of key performance indicators related to the safety and effectiveness of ingestible capsule procedures. Lastly, the present challenges and outlook are summarized with respect to the risks to health, clinical testing and approval process, and technology adoption by patients and clinicians.

Poor Quality of Small Bowel Capsule Endoscopy Images Has a Significant Negative Effect in the Diagnosis of Small Bowel Malignancy

Background and Aims

Capsule endoscopy (CE) is a visual modality; hence, diagnosis relies on image quality. We studied the contribution of image parameters to visualization quality and their effect on diagnostic certainty of small bowel (SB) lesions.

Methods

Five clear CE images of common SB pathology – two vascular lesions, two inflammatory, one polyp – were processed for three image parameters to simulate poor SB conditions: opacity (color-matched to luminal content; 10–90%, 10% increments); blurriness (radius 1–10 pixels; one pixel increments); and contrast (−50-50%; 10% increments). Nine expert readers evaluated whether images were adequate for diagnosis. Points where perception of image quality changed significantly were determined for each parameter. Three further sets of SBCE images (vascular, inflammatory, and neoplastic lesions; nine images/set) were processed for four points/parameters. Twenty experienced/expert CE readers reviewed these images.

Results

The negative effects of opacity in diagnostic certainty were mostly evident in images of neoplasia; images of vascular and inflammatory lesions were less affected. Similar results were observed with increasing blur radius, simulating movement, and poor focus. The proportions of readers finding vascular and inflammatory images adequate for diagnosis did not drop significantly at wider blur radii, while images of neoplasia were quickly deemed inadequate. Low contrast had a greater negative effect than high, most consistently in neoplastic lesions.

Conclusion

Poor visualization quality in all parameters affected mostly neoplastic lesions. Software to increase contrast and sharpen images can improve visualization quality; smart frame rate adaptation could improve the number of high-quality frames obtained. Thoroughness in SB cleansing is most important when there is a suspicion of neoplasia.

Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network

BACKGROUND AND AIMS

Protruding lesions of the small bowel vary in wireless capsule endoscopy (WCE) images, and their automatic detection may be difficult. We aimed to develop and test a deep learning-based system to automatically detect protruding lesions of various types in WCE images.

METHODS

We trained a deep convolutional neural network (CNN), using 30,584 WCE images of protruding lesions from 292 patients. We evaluated CNN performance by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, using an independent set of 17,507 test images from 93 patients, including 7507 images of protruding lesions from 73 patients.

RESULTS

The developed CNN analyzed 17,507 images in 530.462 seconds. The AUC for detection of protruding lesions was 0.911 (95% confidence interval [Cl], 0.9069-0.9155). The sensitivity and specificity of the CNN were 90.7% (95% CI, 90.0%-91.4%) and 79.8% (95% CI, 79.0%-80.6%), respectively, at the optimal cut-off value of 0.317 for probability score. In a subgroup analysis of the category of protruding lesions, the sensitivities were 86.5%, 92.0%, 95.8%, 77.0%, and 94.4% for the detection of polyps, nodules, epithelial tumors, submucosal tumors, and venous structures, respectively. In individual patient analyses (n = 73), the detection rate of protruding lesions was 98.6%.

CONCLUSION

We developed and tested a new computer-aided system based on a CNN to automatically detect various protruding lesions in WCE images. Patient-level analyses with larger cohorts and efforts to achieve better diagnostic performance are necessary in further studies.

PillCam SB3 vs. PillCam SB2: Can technologic advances in capsule endoscopy improve diagnostic yield in patients with small bowel bleeding?

INTRODUCTION AND OBJECTIVE

The SB3 capsule endoscopy system has better image resolution and the capacity to increase the number of images from 2 to 6 frames per second. Small bowel bleeding is the most common indication. The aim To determine if the advances in capsule endoscopy technology increase diagnostic yield in cases of small bowel bleeding, according to the Saurin classification.

MATERIAL AND METHODS

A retrospective, observational, analytic, cross-sectional study included 100 SB2 capsule endoscopies and 100 SB3 capsule endoscopies in patients that presented with small bowel bleeding. The findings obtained with both systems were evaluated. The lesions identified by the two capsules were categorized using the Saurin classification. The relation between the lesions identified with the SB3 and those found with the SB2 was identified through a logistic regression analysis.

RESULTS

In the SB2 capsule endoscopy group, 60% were women, patient age was 59 years (42.2, 73), and intestinal transit time was 271min (182, 353). In the SB3 group, 57% were women, patient age was 60 years (42.5, 73), and intestinal transit time was 277min (182, 352). There were no significant differences in the identification of P0 and P2 lesions between the two systems. The SB3 capsule endoscope identified more P1 lesions (p=0.020, OR: 2.35, 95% CI:1.12-4.90). There was no significant difference in relation to location of the lesions in the small bowel.

CONCLUSIONS

A greater number of P1 lesions were detected through the technologic advances made in SB3 capsule endoscopy, but the diagnostic yield for P2 lesions was not modified.