Current status and limitations of artificial intelligence in colonoscopy

Hann, Alexander; Troya, Joel; Fitting, Daniel

doi:10.1002/ueg2.12108

Cited by 38 publications

(22 citation statements)

References 51 publications

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“…The broad implementation of colonoscopy-based screening has had a major public health impact and is currently being recommended by international medical guidelines 11 . AI has been successfully used to automate this screening procedure by automatically delineating adenomas in colonoscopy image data 12 – 16 . These approaches have to date yielded multiple commercially available deep learning-based systems for polyp (adenoma) detection which are successfully being applied in endoscopy routine across the world.…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Buendgens

Cifci

Laleh

et al. 2022

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Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithms for polyp detection, but application of AI beyond this specific task is limited by the high cost of manual annotations. Here, we show that a weakly supervised AI can be trained on data from a clinical routine database to learn visual patterns of GI diseases without any manual labeling or annotation. We trained a deep neural network on a dataset of N = 29,506 gastroscopy and N = 18,942 colonoscopy examinations from a large endoscopy unit serving patients in Germany, the Netherlands and Belgium, using only routine diagnosis data for the 42 most common diseases. Despite a high data heterogeneity, the AI system reached a high performance for diagnosis of multiple diseases, including inflammatory, degenerative, infectious and neoplastic diseases. Specifically, a cross-validated area under the receiver operating curve (AUROC) of above 0.70 was reached for 13 diseases, and an AUROC of above 0.80 was reached for two diseases in the primary data set. In an external validation set including six disease categories, the AI system was able to significantly predict the presence of diverticulosis, candidiasis, colon and rectal cancer with AUROCs above 0.76. Reverse engineering the predictions demonstrated that plausible patterns were learned on the level of images and within images and potential confounders were identified. In summary, our study demonstrates the potential of weakly supervised AI to generate high-performing classifiers and identify clinically relevant visual patterns based on non-annotated routine image data in GI endoscopy and potentially other clinical imaging modalities.

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Section: Introductionmentioning

confidence: 99%

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Buendgens

Cifci

Laleh

et al. 2022

Sci Rep

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“…If a false detection occurs in addition to a relevant finding, the examiner's attention may be distracted, leading to missed findings in the worst case. 9 Daily use of CADe systems shows that endoscopic interventions (especially biopsies and polypectomies) lead to many false activations of CADe systems. In this case, false positive activations occur due to the inserted instruments (forceps, needle, snare), but also due to intervention on the mucosa itself (injection, resection, clipping).…”

Section: Introductionmentioning

confidence: 99%

“…These false markings can affect the examiner's concentration. If a false detection occurs in addition to a relevant finding, the examiner's attention may be distracted, leading to missed findings in the worst case 9 …”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions

et al. 2022

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Background The efficiency of artificial intelligence as computer‐aided detection (CADe) systems for colorectal polyps has been demonstrated in several randomized trials. However, CADe systems generate many distracting detections, especially during interventions such as polypectomies. Those distracting CADe detections are often induced by the introduction of snares or biopsy forceps as the systems have not been trained for such situations. In addition, there are a significant number of non‐false but not relevant detections, since the polyp has already been previously detected. All these detections have the potential to disturb the examiner's work. Objectives Development and evaluation of a convolutional neuronal network that recognizes instruments in the endoscopic image, suppresses distracting CADe detections, and reliably detects endoscopic interventions. Methods A total of 580 different examination videos from 9 different centers using 4 different processor types were screened for instruments and represented the training dataset (519,856 images in total, 144,217 contained a visible instrument). The test dataset included 10 full‐colonoscopy videos that were analyzed for the recognition of visible instruments and detections by a commercially available CADe system (GI Genius, Medtronic). Results The test dataset contained 153,623 images, 8.84% of those presented visible instruments (12 interventions, 19 instruments used). The convolutional neuronal network reached an overall accuracy in the detection of visible instruments of 98.59%. Sensitivity and specificity were 98.55% and 98.92%, respectively. A mean of 462.8 frames containing distracting CADe detections per colonoscopy were avoided using the convolutional neuronal network. This accounted for 95.6% of all distracting CADe detections. Conclusions Detection of endoscopic instruments in colonoscopy using artificial intelligence technology is reliable and achieves high sensitivity and specificity. Accordingly, the new convolutional neuronal network could be used to reduce distracting CADe detections during endoscopic procedures. Thus, our study demonstrates the great potential of artificial intelligence technology beyond mucosal assessment.

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“…At present, a topic that is becoming highly relevant in the application of deep learning (AI) in health are the so-called computer-aided detection (CADe) and diagnosis (CADx) in which, through a system based on recognition of patterns in images, meaning lesions in complex structures can be identified and classified through the different shapes and intensity levels in the pixels; some examples of this are the CADe/CADx systems developed for detection of lung and breast cancer, colonoscopy, etc. [5][6][7][8], but there are still no applications thus far for the classification and diagnosis of cardiac arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification

et al. 2021

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Arrhythmias are the most common events tracked by a physician. The need for continuous monitoring of such events in the ECG has opened the opportunity for automatic detection. Intra- and inter-patient paradigms are the two approaches currently followed by the scientific community. The intra-patient approach seems to resolve the problem with a high classification percentage but requires a physician to label key samples. The inter-patient makes use of historic data of different patients to build a general classifier, but the inherent variability in the ECG’s signal among patients leads to lower classification percentages compared to the intra-patient approach. In this work, we propose a new unsupervised algorithm that adapts to every patient using the heart rate and morphological features of the ECG beats to classify beats between supraventricular origin and ventricular origin. The results of our work in terms of F-score are 0.88, 0.89, and 0.93 for the ventricular origin beats for three popular ECG databases, and around 0.99 for the supraventricular origin for the same databases, comparable to supervised approaches presented in other works. This paper presents a new path to make use of ECG data to classify heartbeats without the assistance of a physician despite the needed improvements.

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Current status and limitations of artificial intelligence in colonoscopy

Cited by 38 publications

References 51 publications

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions

A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification

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