Published October 6, 2022 | Version v1
Publication Open

Rhode Island gastroenterology video capsule endoscopy data set

  • 1. Brown University
  • 2. Providence College
  • 3. Chulabhorn Hospital
  • 4. Srinakharinwirot University

Description

Complete endoscopic evaluation of the small bowel is challenging due to its length and anatomy. Although several advances have been made to achieve diagnostic and therapeutic goals, including double-balloon enteroscopy, single-balloon enteroscopy, and spiral enteroscopy, video capsule endoscopy (VCE) remains the least invasive tool for complete visualization of the small bowel and is the preferred method for initial diagnostic evaluation. At present, interpretation of VCE data requires manual annotation of landmarks and abnormalities in recorded videos, which can be time consuming. Computer-assisted diagnostic systems using artificial intelligence may help to optimize VCE reading efficiency by reducing the need for manual annotation. Here we present a large VCE data set compiled from studies performed at two United States hospitals in Providence, Rhode Island, including 424 VCE studies and 5,247,588 total labeled images. In conjunction with existing published data sets, these files may aid in the development of algorithms to further improve VCE.

⚠️ This is an automatic machine translation with an accuracy of 90-95%

Translated Description (Arabic)

يعد التقييم الكامل بالمنظار للوعاء الصغير أمرًا صعبًا بسبب طوله وتشريحه. على الرغم من إحراز العديد من التطورات لتحقيق الأهداف التشخيصية والعلاجية، بما في ذلك تنظير الأمعاء بالون مزدوج، وتنظير الأمعاء بالون واحد، وتنظير الأمعاء الحلزوني، يظل تنظير كبسولة الفيديو (VCE) الأداة الأقل توغلاً للتصور الكامل للأمعاء الدقيقة وهو الطريقة المفضلة للتقييم التشخيصي الأولي. في الوقت الحاضر، يتطلب تفسير بيانات VCE شرحًا يدويًا للمعالم والعيوب في مقاطع الفيديو المسجلة، والتي يمكن أن تستغرق وقتًا طويلاً. قد تساعد أنظمة التشخيص بمساعدة الكمبيوتر باستخدام الذكاء الاصطناعي في تحسين كفاءة قراءة VCE من خلال تقليل الحاجة إلى التعليق التوضيحي اليدوي. نقدم هنا مجموعة بيانات VCE كبيرة تم تجميعها من الدراسات التي أجريت في مستشفيين أمريكيين في بروفيدنس، رود آيلاند، بما في ذلك 424 دراسة VCE و 5,247,588 صورة إجمالية مصنفة. بالتزامن مع مجموعات البيانات المنشورة الحالية، قد تساعد هذه الملفات في تطوير الخوارزميات لزيادة تحسين VCE.

Translated Description (English)

Complete endoscopic evaluation of the small bowl is challenging due to its length and anatomy. Although several advances have been made to achieve diagnostic and therapeutic goals, including double-balloon enteroscopy, single-balloon enteroscopy, and spiral enteroscopy, video capsule endoscopy (VCE) remains the least invasive tool for complete visualization of the small bowel and is the preferred method for initial diagnostic evaluation. At present, interpretation of VCE data requires manual annotation of landmarks and abnormalities in recorded videos, which can be time consuming. Computer-assisted diagnostic systems using artificial intelligence may help to optimize VCE reading efficiency by reducing the need for manual annotation. Here we present a large VCE data set compiled from studies performed at two United States hospitals in Providence, Rhode Island, including 424 VCE studies and 5,247,588 total labeled images. In conjunction with existing published data sets, these files may aid in the development of algorithms to further improve VCE.

Translated Description (French)

Complete endoscopic evaluation of the small bowel is challenging due to its length and anatomy. Although several advances have been made to achieve diagnostic and therapeutic goals, including double-balloon enteroscopy, single-balloon enteroscopy, and spiral enteroscopy, video capsules endoscopy (VCE) remains the least invasive tool for complete visualization of the small bowel and is the preferred method for initial diagnostic evaluation. At present, interpretation of VCE data requires manual annotation of landmarks and abnormalities in recorded videos, which can be time consuming. Computer-assisted diagnostic systems using artificial intelligence may help to optimize VCE reading efficiency by reducing the need for manual annotation. Here we present a large VCE data set compiled from studies performed at two United States hospitals in Providence, Rhode Island, including 424 VCE studies and 5,247,588 total labeled images. En conjonction avec les ensembles de données publiés existants, ces fichiers peuvent aider au développement d'algorithmes pour améliorer la VCE.

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Additional details

Additional titles

Translated title (Arabic)
مجموعة بيانات التنظير الداخلي لكبسولات فيديو أمراض الجهاز الهضمي في رود آيلاند
Translated title (English)
Rhode Island gastroenterology video capsules endoscopy data set
Translated title (French)
Rhode Island gastro-entérologie video capsule endoscopy data set

Identifiers

Other
https://openalex.org/W4302292921
DOI
10.1038/s41597-022-01726-3

GreSIS Basics Section

Is Global South Knowledge
Yes
Country
Thailand

References

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  • https://openalex.org/W4302292921