A multi-stage machine learning model for diagnosis of esophageal manometry

Kou, Wenjun; Carlson, Dustin A.; Baumann, Alexandra J.; Donnan, Erica; Schauer, Jacob M.; Etemadi, Mozziyar; Pandolfino, John E.

doi:10.1016/j.artmed.2021.102233

Cited by 15 publications

(14 citation statements)

References 20 publications

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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%

“…Another study performed by Kou et al [ 4 , 5 ] on automated detection of EMDs using raw multi-swallow pictures collected from esophageal HRM, showed good accuracy by using machine learning techniques and deep-learning models with a dataset of 1741 patients.…”

Section: Discussionmentioning

confidence: 99%

“…The datasets were generated by two human specialists from Romania’s “Iuliu Hatieganu” University of Medicine and Pharmacy Cluj-Napoca. Previous studies showed that the best inter-observer agreement when interpreting esophageal-HRM studies is for achalasia, while for other motility disorders the agreement is only ‘fair-moderate’ [ 3 , 4 ]. Therefore, the images were labeled by the Romanian specialists in collaboration with two specialists from Italy.…”

Section: Methodsmentioning

confidence: 99%

“…Unfortunately, although AI adoption is continuing its steady rise, the applications in esophageal manometry are limited. Only a few studies exist [ 3 , 4 , 5 ], and thus far, these applications are not used in a clinical setting.…”

Section: Introductionmentioning

confidence: 99%

“…Authors from the article [ 4 ] used a more complex solution. They built a pipeline of multiple CNN models and combined the results to obtain the final diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Authors from the article [ 4 ] used a more complex solution. They built a pipeline of multiple CNN models and combined the results to obtain the final diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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