Automatic Detection of Small Intestinal Hookworms in Capsule Endoscopy Images Based on a Convolutional Neural Network

Effective Laboratory Diagnosis of Parasitic Infections of the Gastrointestinal Tract: Where, When, How, and What Should We Look For?

(1) Introduction: Gastrointestinal parasites (GIPs) are one of the most common causes of disease in the world. Clinical diagnosis of most parasitic diseases is difficult because they do not produce characteristic symptoms. (2) Methods: The PubMed, Science Direct, and Wiley Online Library medical databases were reviewed using the following phrases: "parasitic infections and diagnostics", "intestinal parasites", "gastrointestinal parasites", "parasitic infections and diagnostics", and their combinations. (3) Results and Conclusions: Correct diagnosis of GIP involves determining the presence of a parasite and establishing a relationship between parasite invasion and disease symptoms. The diagnostic process should consider the possibility of the coexistence of infection with several parasites at the same time. In such a situation, diagnostics should be planned with consideration of their frequency in each population and the local epidemiological situation. The importance of the proper interpretation of laboratory test results, based on good knowledge of the biology of the parasite, should be emphasized. The presence of the parasite may not be causally related to the disease symptoms. Due to wide access to laboratories, patients often decide to perform tests themselves without clinical justification. Research is carried out using various methods which are often unreliable. This review briefly covers current laboratory methods for diagnosing the most common gastrointestinal parasitic diseases in Europe. In particular, we provide useful information on the following aspects: (i) what to look for and where to look for it (suitability of feces, blood, duodenal contents, material taken from endoscopy or biopsy, tissue samples, and locations for searching for eggs, cysts, parasites, parasite genetic material, and characteristics of immune responses indicating parasitic infections); (ii) when material should be collected for diagnosis and/or to check the effectiveness of treatment; (iii) how-that is, by what methods-laboratory diagnostics should be carried out. Here, the advantages and disadvantages of direct and indirect methods of detecting parasites will be discussed. False-positive or false-negative results are a problem facing many tests. Available tests have different sensitivities and specificities. Therefore, especially in doubtful situations, tests for the presence of the pathogen should be performed using various available methods. It is important that the methods used make it possible to distinguish an active infection from a past infection. Finally, we present laboratory "case reports", in which we will discuss the diagnostic procedure that allows for the successful identification of parasites. Additionally, we briefly present the possibilities of using artificial intelligence to improve the effectiveness of diagnosing parasitic diseases.

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process.

Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm

BACKGROUND

In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE.

METHODS

A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively.

RESULTS

The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001).

CONCLUSIONS

CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.

Artificial intelligence in gastroenterology: A narrative review

Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.

Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video)

The artificial intelligence (AI)-assisted endoscopic detection of early gastric cancer (EGC) has been preliminarily developed. The currently used algorithms still exhibit limitations of large calculation and low-precision expression. The present study aimed to develop an endoscopic automatic detection system in EGC based on a mask region-based convolutional neural network (Mask R-CNN) and to evaluate the performance in controlled trials. For this purpose, a total of 4,471 white light images (WLIs) and 2,662 narrow band images (NBIs) of EGC were obtained for training and testing. In total, 10 of the WLIs (videos) were obtained prospectively to examine the performance of the RCNN system. Furthermore, 400 WLIs were randomly selected for comparison between the Mask R-CNN system and doctors. The evaluation criteria included accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The results revealed that there were no significant differences between the pathological diagnosis with the Mask R-CNN system in the WLI test (χ² = 0.189, P=0.664; accuracy, 90.25%; sensitivity, 91.06%; specificity, 89.01%) and in the NBI test (χ² = 0.063, P=0.802; accuracy, 95.12%; sensitivity, 97.59%). Among 10 WLI real-time videos, the speed of the test videos was up to 35 frames/sec, with an accuracy of 90.27%. In a controlled experiment of 400 WLIs, the sensitivity of the Mask R-CNN system was significantly higher than that of experts (χ² = 7.059, P=0.000; 93.00% VS 80.20%), and the specificity was higher than that of the juniors (χ² = 9.955, P=0.000, 82.67% VS 71.87%), and the overall accuracy rate was higher than that of the seniors (χ² = 7.009, P=0.000, 85.25% VS 78.00%). On the whole, the present study demonstrates that the Mask R-CNN system exhibited an excellent performance status for the detection of EGC, particularly for the real-time analysis of WLIs. It may thus be effectively applied to clinical settings.