Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output
Background and Aims: Inadequate bowel preparation which occurs in 25% of colonoscopies is a major barrier to the effectiveness of screening for colorectal cancer. We aim to develop an artificial intelligence (machine learning) algorithm to assess photos of stool output after bowel preparation to pre...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Gastro Hep Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277257232400150X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850192592819453952 |
|---|---|
| author | Chethan Ramprasad Divya Saini Henry Del Carmen Lev Krasnovsky Rajat Chandra Ryan Mcgregor Russell T. Shinohara Eric Eaton Meghna Gummadi Shivan Mehta James D. Lewis |
| author_facet | Chethan Ramprasad Divya Saini Henry Del Carmen Lev Krasnovsky Rajat Chandra Ryan Mcgregor Russell T. Shinohara Eric Eaton Meghna Gummadi Shivan Mehta James D. Lewis |
| author_sort | Chethan Ramprasad |
| collection | DOAJ |
| description | Background and Aims: Inadequate bowel preparation which occurs in 25% of colonoscopies is a major barrier to the effectiveness of screening for colorectal cancer. We aim to develop an artificial intelligence (machine learning) algorithm to assess photos of stool output after bowel preparation to predict inadequate bowel preparation before colonoscopy. Methods: Patients were asked to text a photo of their stool in the commode when they believed that they neared completion of their colonoscopy bowel preparation. Boston Bowel Preparation Scores of 7 and below were labeled as inadequate or fair. Boston Bowel Preparation Scores of 8 and 9 were considered good. A binary classification image-based machine learning algorithm was designed. Results: In a test set of 61 images, the binary classification machine learning algorithm was able to distinguish inadequate/fair preparation from good preparation with a positive predictive value of 78.6% and a negative predictive value of 60.8%. In a test set of 56 images, the algorithm was able to distinguish normal colonoscopy duration (<25 minutes) from long colonoscopy duration (>25 minutes) with a positive predictive value of 78.6% and a negative predictive value of 65.5%. Conclusion: Patients are willing to submit photos of their stool output during bowel preparation through text messages before colonoscopy. This machine learning algorithm demonstrates the ability to predict inadequate/fair preparation from good preparation based on image classification of stool output. It was less accurate to predict long duration of colonoscopy. |
| format | Article |
| id | doaj-art-6ef3176a49ce4f36b63b2a33d2f71e3f |
| institution | OA Journals |
| issn | 2772-5723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Gastro Hep Advances |
| spelling | doaj-art-6ef3176a49ce4f36b63b2a33d2f71e3f2025-08-20T02:14:30ZengElsevierGastro Hep Advances2772-57232025-01-014210055610.1016/j.gastha.2024.09.011Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool OutputChethan Ramprasad0Divya Saini1Henry Del Carmen2Lev Krasnovsky3Rajat Chandra4Ryan Mcgregor5Russell T. Shinohara6Eric Eaton7Meghna Gummadi8Shivan Mehta9James D. Lewis10Division of Gastroenterology, University of Pennsylvania, Philadelphia, Pennsylvania; Correspondence: Address correspondence to: Chethan Ramprasad, MD, Division of Gastroenterology, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104.Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PennsylvaniaPerelman School of Medicine at the University of Pennsylvania, Philadelphia, PennsylvaniaPerelman School of Medicine at the University of Pennsylvania, Philadelphia, PennsylvaniaPerelman School of Medicine at the University of Pennsylvania, Philadelphia, PennsylvaniaPerelman School of Medicine at the University of Pennsylvania, Philadelphia, PennsylvaniaPerelman School of Medicine at the University of Pennsylvania, Center for Clinical Epidemiology and Biostatistics, Philadelphia, PennsylvaniaDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PennsylvaniaDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PennsylvaniaDivision of Gastroenterology, University of Pennsylvania, Philadelphia, PennsylvaniaDivision of Gastroenterology, University of Pennsylvania, Philadelphia, PennsylvaniaBackground and Aims: Inadequate bowel preparation which occurs in 25% of colonoscopies is a major barrier to the effectiveness of screening for colorectal cancer. We aim to develop an artificial intelligence (machine learning) algorithm to assess photos of stool output after bowel preparation to predict inadequate bowel preparation before colonoscopy. Methods: Patients were asked to text a photo of their stool in the commode when they believed that they neared completion of their colonoscopy bowel preparation. Boston Bowel Preparation Scores of 7 and below were labeled as inadequate or fair. Boston Bowel Preparation Scores of 8 and 9 were considered good. A binary classification image-based machine learning algorithm was designed. Results: In a test set of 61 images, the binary classification machine learning algorithm was able to distinguish inadequate/fair preparation from good preparation with a positive predictive value of 78.6% and a negative predictive value of 60.8%. In a test set of 56 images, the algorithm was able to distinguish normal colonoscopy duration (<25 minutes) from long colonoscopy duration (>25 minutes) with a positive predictive value of 78.6% and a negative predictive value of 65.5%. Conclusion: Patients are willing to submit photos of their stool output during bowel preparation through text messages before colonoscopy. This machine learning algorithm demonstrates the ability to predict inadequate/fair preparation from good preparation based on image classification of stool output. It was less accurate to predict long duration of colonoscopy.http://www.sciencedirect.com/science/article/pii/S277257232400150XArtificial IntelligenceColonoscopyBowel PreparationTechnology Positive |
| spellingShingle | Chethan Ramprasad Divya Saini Henry Del Carmen Lev Krasnovsky Rajat Chandra Ryan Mcgregor Russell T. Shinohara Eric Eaton Meghna Gummadi Shivan Mehta James D. Lewis Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output Gastro Hep Advances Artificial Intelligence Colonoscopy Bowel Preparation Technology Positive |
| title | Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output |
| title_full | Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output |
| title_fullStr | Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output |
| title_full_unstemmed | Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output |
| title_short | Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output |
| title_sort | text message system for the prediction of colonoscopy bowel preparation adequacy before colonoscopy an artificial intelligence image classification algorithm based on images of stool output |
| topic | Artificial Intelligence Colonoscopy Bowel Preparation Technology Positive |
| url | http://www.sciencedirect.com/science/article/pii/S277257232400150X |
| work_keys_str_mv | AT chethanramprasad textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT divyasaini textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT henrydelcarmen textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT levkrasnovsky textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT rajatchandra textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT ryanmcgregor textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT russelltshinohara textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT ericeaton textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT meghnagummadi textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT shivanmehta textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput AT jamesdlewis textmessagesystemforthepredictionofcolonoscopybowelpreparationadequacybeforecolonoscopyanartificialintelligenceimageclassificationalgorithmbasedonimagesofstooloutput |