HillKING: Hill-grade Knowledge Via Integrated Neural-network for Gastroscopy
Study Details
Study Description
Brief Summary
The Hill classification, also known as the Hill grade, is a system used to classify the severity of gastroesophageal valve incompetence, specifically related to gastroesophageal reflux disease (GERD) and hiatal hernia. This study aims to compare the ability of physicians versus an AI model to asses the Hill grade during gastroscopy.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
Objective:
The primary goal of this study is to compare the accuracy in determining the Hill classification during gastroscopy between an artificial intelligence (AI) based system and physicians performing the examination. Secondary outcomes include evaluation of the per-class accuracy and other statistical measures such as precision, recall and f1 score.
Study Design:
Singlecenter, endoscopist blinded study. The model considered in a previous study achieved a mean accuracy of 88%. All participants initially attended a lecture serving as a refresher regarding the Hill classification. Subsequently, physicians were asked to provide the Hill classification for test images expert annotated images depicting different Hill grades, achieving mean accuracy of 72%. Thus 127 paired measurements are required. Taking patient drop-out into consideration, at least 159 patients need to be recruited. Upon examination of the flap-valve during endoscopy, the physician is required to store an image of the flap-valve during retroflexion, which is part of the standard procedure, based on which they determine the Hill classification. The prediction of the AI model on this image is considered the model output and is considered the model's output. A group of three expert endoscopists determines the Hill classification for each image, based on majority vote, which is treated as the gold standard.
AI setup and limitations:
There are no limitations caused by the AI. The method performs a frame-by-frame analysis of the recording. These images are parsed from the AI based system in order to obtain predictions. The only interactions required with the method is a button press that initiates the examination recording process and a second button press to terminate the recording. This is performed at the beginning and end of the examination respectively. The model used in this study is an updated version of the model reported in a preliminary study, that has been trained with more data together with an auxiliary output for predicting if the Hill classification is relevant to the shown image.
Study population:
All adult patients appointed for gastroscopy that do not match the exclusion criteria will be asked for informed consent. Exclusion criteria include previous surgical interventions or altered anatomy that prevents the proper examination of the flap valve, examinations where the flap-valve is not inspected, and examinations where the expert committee does not produce a majority vote.
Intervention:
The physician performs the examination as usual. Upon inspection of the flap valve, the physician captures an image of the examination, as usual, and gives their assessment of the Hill grade. The output of the model for the same image is considered the model prediction. The physician is blinded to the model's prediction.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Intervention arm All patients within the study are included in the intervention arm: The Hill classification is determined by the physician and the AI method. |
Device: EndoMind
The EndoMind system is equipped with an AI model for predicting the Hill grade during gastroscopy.
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Outcome Measures
Primary Outcome Measures
- Comparison of correct and erroneous assessments of the Hill grade between physicians and AI method. [Through study completion, an average of 5 months]
Binary assessment of physician vs AI correct and erroneous predictions.
Secondary Outcome Measures
- Per Hill grade comparison of correct and erroneous assessments between physicians and AI method. [Through study completion, an average of 5 months]
Description: The correct and erroneous predictions for each specific Hill class.
- Per Hill grade accuracy, precision, and recall of the endoscopists and AI method assessments. [Through study completion, an average of 5 months]
The accuracy, precision, and recall statistics for each class (1v0) over the four different Hill classes.
- Distance for label assessment from gold standard label. [Through study completion, an average of 5 months]
Comparison of the distance between the gold standard label and the label assigned by the phyisician and AI method.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Adult patients (>18 years)
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Scheduled gastroscopy
Exclusion Criteria:
Examination level
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Previous surgical interventions or altered anatomy that prevents the proper examination of the flap valve
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Flap-valve not inspected
Data Level:
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Image during flap-valve inspection not stored
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Expert committee not resulting in a majority vote
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Universitätsklinikum Würzburg | Würzburg | Bayern | Germany | 97080 |
Sponsors and Collaborators
- Wuerzburg University Hospital
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- AI04