Niranchan Paskaranandavadivel1,2, Anthony Y Lin3, Leo K Cheng4,5, Ian Bissett3,6, Andrew Lowe7, John Arkwright8, Saeed Mollaee4, Phil G Dinning9, Gregory O'Grady4,3,6. 1. Auckland Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand. nira.pask@auckland.ac.nz. 2. Department of Surgery, University of Auckland, Auckland, New Zealand. nira.pask@auckland.ac.nz. 3. Department of Surgery, University of Auckland, Auckland, New Zealand. 4. Auckland Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand. 5. Vanderbilt University, Nashville, TN, USA. 6. Auckland City Hospital, Auckland, New Zealand. 7. Institute of Biomedical Engineering, Auckland University of Technology, Auckland, New Zealand. 8. College of Science and Engineering, Flinders University, Adelaide, Australia. 9. Departments of Gastroenterology & Surgery Flinders Medical Centre, Flinders University, Adelaide, Australia.
Abstract
RATIONALE: Colonic high-resolution manometry (cHRM) is an emerging clinical tool for defining colonic function in health and disease. Current analysis methods are conducted manually, thus being inefficient and open to interpretation bias. OBJECTIVE: The main objective of the study was to build an automated system to identify propagating contractions and compare the performance to manual marking analysis. METHODS: cHRM recordings were performed on 5 healthy subjects, 3 subjects with diarrhea-predominant irritable bowel syndrome, and 3 subjects with slow transit constipation. Two experts manually identified propagating contractions, from five randomly selected 10-min segments from each of the 11 subjects (72 channels per dataset, total duration 550 min). An automated signal processing and detection platform was developed to compare its effectiveness to manually identified propagating contractions. In the algorithm, individual pressure events over a threshold were identified and were then grouped into a propagating contraction. The detection platform allowed user-selectable thresholds, and a range of pressure thresholds was evaluated (2 to 20 mmHg). KEY RESULTS: The automated system was found to be reliable and accurate for analyzing cHRM with a threshold of 15 mmHg, resulting in a positive predictive value of 75%. For 5-h cHRM recordings, the automated method takes 22 ± 2 s for analysis, while manual identification would take many hours. CONCLUSIONS: An automated framework was developed to filter, detect, quantify, and visualize propagating contractions in cHRM recordings in an efficient manner that is reliable and consistent.
RATIONALE: Colonic high-resolution manometry (cHRM) is an emerging clinical tool for defining colonic function in health and disease. Current analysis methods are conducted manually, thus being inefficient and open to interpretation bias. OBJECTIVE: The main objective of the study was to build an automated system to identify propagating contractions and compare the performance to manual marking analysis. METHODS: cHRM recordings were performed on 5 healthy subjects, 3 subjects with diarrhea-predominant irritable bowel syndrome, and 3 subjects with slow transit constipation. Two experts manually identified propagating contractions, from five randomly selected 10-min segments from each of the 11 subjects (72 channels per dataset, total duration 550 min). An automated signal processing and detection platform was developed to compare its effectiveness to manually identified propagating contractions. In the algorithm, individual pressure events over a threshold were identified and were then grouped into a propagating contraction. The detection platform allowed user-selectable thresholds, and a range of pressure thresholds was evaluated (2 to 20 mmHg). KEY RESULTS: The automated system was found to be reliable and accurate for analyzing cHRM with a threshold of 15 mmHg, resulting in a positive predictive value of 75%. For 5-h cHRM recordings, the automated method takes 22 ± 2 s for analysis, while manual identification would take many hours. CONCLUSIONS: An automated framework was developed to filter, detect, quantify, and visualize propagating contractions in cHRM recordings in an efficient manner that is reliable and consistent.
Authors: R Vather; G O'Grady; A Y Lin; P Du; C I Wells; D Rowbotham; J Arkwright; L K Cheng; P G Dinning; I P Bissett Journal: Br J Surg Date: 2018-04-14 Impact factor: 6.939
Authors: P G Dinning; L Wiklendt; L Maslen; I Gibbins; V Patton; J W Arkwright; D Z Lubowski; G O'Grady; P A Bampton; S J Brookes; M Costa Journal: Neurogastroenterol Motil Date: 2014-08-11 Impact factor: 3.598
Authors: P G Dinning; L Wiklendt; L Maslen; V Patton; H Lewis; J W Arkwright; D A Wattchow; D Z Lubowski; M Costa; P A Bampton Journal: Neurogastroenterol Motil Date: 2015-01-03 Impact factor: 3.598
Authors: Cameron I Wells; Sameer Bhat; Nira Paskaranandavadivel; Anthony Y Lin; Ryash Vather; Chris Varghese; James A Penfold; David Rowbotham; Phil G Dinning; Ian P Bissett; Greg O'Grady Journal: Physiol Rep Date: 2021-11
Authors: Cameron I Wells; Nira Paskaranandavadivel; Peng Du; James A Penfold; Armen Gharibans; Ian P Bissett; Greg O'Grady Journal: Physiol Rep Date: 2021-07