Deep learning for tracing esophageal motility function over time

Wang, Zheng; Hou, Muzhou; Lu, Yan; Dai, Yuzhuo; Yin, Yani; Liu, Xiaowei

doi:10.1016/j.cmpb.2021.106212

Cited by 10 publications

(10 citation statements)

References 16 publications

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“…In some cases, the distinction between the two entities might be difficult even by the experienced examiner [4,5,13]. Overall accuracy and precision (94%) and recall (93%) with our system were very good and are comparable to other solutions previously published [14][15][16][17].…”

Section: Resultssupporting

confidence: 72%

“…Using a decision support system and based on the similarity between the database and patients' parameters, the correct diagnoses were observed in 86% of cases. In another study [17], the authors tried to make real-time predictions on the the esophageal function. It could be normal, or there could be a minor or a major motility disorder.…”

Section: Resultsmentioning

confidence: 99%

“…The tracings were classified based on Chicago classification v3.0, which was available when the tests were performed. The other studies published on this topic, also used this version of the Chicago classification [8,[14][15][16][17]. The updated version v. 4.0 has already addressed some of the weakness of the former version [12].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence

Popa

Surdea-Blaga

Dumitraşcu

et al. 2022

JGLD

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Background and Aims: High-resolution esophageal manometry (HREM) is the gold standard procedure used for the diagnosis of esophageal motility disorders (EMD). Artificial intelligence (AI) might provide an efficient solution for the automatic diagnosis of EMD by improving the subjective interpretation of HREM images. The aim of our study was to develop an AI-based system, using neural networks, for the automatic diagnosis of HREM images, based on one wet swallow raw image. Methods: In the first phase of the study, the manometry recordings of our patients were retrospectively analyzed by three experienced gastroenterologists, to verify and confirm the correct diagnosis. In the second phase of the study raw images were used to train an artificial neural network. We selected only those tracings with ten test swallows that were available for analysis, including a total of 1570 images. We had 10 diagnosis categories, as follows: normal, type I achalasia, type II achalasia, type III achalasia, esophago-gastric junction outflow obstruction, jackhammer oesophagus, absent contractility, distal esophageal spasm, ineffective esophageal motility, and fragmented peristalsis, based on Chicago classification v3.0 for EMDs. Results: The raw images were cropped, binarized, and automatically divided in 3 parts: training, testing, validation. We used Inception V3 CNN model, pre-trained on ImageNet. We developed a custom classification layer, that allowed the CNN to classify each wet swallow image from the HREM system into one of the diagnosis categories mentioned above. Our algorithm was highly accurate, with an overall precision of more than 93%. Conclusion: Our neural network approach using HREM images resulted in a high accuracy automatic diagnosis of EMDs.

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Section: Resultssupporting

confidence: 72%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence

Popa

Surdea-Blaga

Dumitraşcu

et al. 2022

JGLD

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“…There is limited research [ 3 , 4 , 5 , 19 , 20 , 21 , 22 , 23 , 24 ] that explored automated diagnosis of EMDs and pharingeal swallows utilizing AI-based methods or automation of the Chicago Classification system, Table 1 . In addition, the most relevant research in this sector is discussed below.…”

Section: Discussionmentioning

confidence: 99%

“…In [ 21 ] we can read about a slightly different problem and solution. The authors of this article built a solution for real-time esophageal motility function tracing.…”

Section: Discussionmentioning

confidence: 99%

Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning

Surdea-Blaga

Sebestyen

Czako

et al. 2022

Sensors

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The goal of this paper is to provide a Machine Learning-based solution that can be utilized to automate the Chicago Classification algorithm, the state-of-the-art scheme for esophageal motility disease identification. First, the photos were preprocessed by locating the area of interest—the precise instant of swallowing. After resizing and rescaling the photos, they were utilized as input for the Deep Learning models. The InceptionV3 Deep Learning model was used to identify the precise class of the IRP. We used the DenseNet201 CNN architecture to classify the images into 5 different classes of swallowing disorders. Finally, we combined the results of the two trained ML models to automate the Chicago Classification algorithm. With this solution we obtained a top-1 accuracy and f1-score of 86% with no human intervention, automating the whole flow, from image preprocessing until Chicago classification and diagnosis.

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Auto-metric distribution propagation graph neural network with a meta-learning strategy for diagnosis of otosclerosis

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Wang

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Appl Intell

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Deep learning for tracing esophageal motility function over time

Cited by 10 publications

References 16 publications

Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence

Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence

Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning

Auto-metric distribution propagation graph neural network with a meta-learning strategy for diagnosis of otosclerosis

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