Endoflip vs high‐definition manometry in the assessment of fecal incontinence: A data‐driven unsupervised comparison
Background How much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High‐definition anorectal manometry (HDAM) and functional luminal imaging probe (Endoflip) are two new techniques to study anal sphincter function. Aims The goal was to compare the diagnostic utility...
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Published in | Neurogastroenterology and motility Vol. 30; no. 12; pp. e13462 - n/a |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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01.12.2018
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Abstract | Background
How much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High‐definition anorectal manometry (HDAM) and functional luminal imaging probe (Endoflip) are two new techniques to study anal sphincter function.
Aims
The goal was to compare the diagnostic utility of HDAM and Endoflip using optimal feature(s) in each modality for FI diagnosis. Methods: Blinded classification was carried out on 70 female subjects (32 FI & 38 controls), using 3 prominent machine‐learning clustering techniques, with 3 distance metrics. For HDAM, descriptive statistics, shape, and textural features characterizing the spatial relationship of pixels in the HDAM high‐pressure zone, and for Endoflip, permutations of pressure and CSA combinations (ie, multiplication, division, or individually) at rest and squeeze were tested.
Results
Intramodality: (a) Endoflip: Best clustering was obtained using the combination of the ratio of CSA over pressure at 40 and 50 mL at rest, which had significantly better specificity (P < 0.001) than using only pressure at 50 mL, no difference in sensitivity (P = 0.68). (b) HDAM: clustering using textural information at rest had significantly higher specificity compared to using only the maximal pressure at rest (P < 0.001). Intermodality: Clustering results using optimal features were not significantly different with respect to sensitivity or specificity (P > 0.05). Optimal Endoflip feature set differed significantly in specificity compared to HDAM maximal pressure at both rest (P < 0.001) and squeeze (P < 0.001).
Conclusion
Defective anal closure function is fairly sensitive and highly specific in diagnosing FI. Using optimal feature sets, HDAM and Endoflip perform in a similar fashion in diagnosing FI, but are not complementary.
High‐definition anal manometry and functional luminal imaging probe (Endoflip) are two novel techniques to assess the anal closure function; however, their sensitivity and specificity in diagnosing fecal incontinence (FI) are not clear. We used machine‐learning tools to determine the ideal parameters provided by HDAM and Endoflip and performed blinded analysis on a group of FI patients and normal subjects to determine the sensitivity and specificity of two techniques in diagnosing FI. Our data show that both of these techniques are moderately sensitive but highly specific in the FI diagnosing, but they are not complimentary. |
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AbstractList | BackgroundHow much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High‐definition anorectal manometry (HDAM) and functional luminal imaging probe (Endoflip) are two new techniques to study anal sphincter function.AimsThe goal was to compare the diagnostic utility of HDAM and Endoflip using optimal feature(s) in each modality for FI diagnosis. Methods: Blinded classification was carried out on 70 female subjects (32 FI & 38 controls), using 3 prominent machine‐learning clustering techniques, with 3 distance metrics. For HDAM, descriptive statistics, shape, and textural features characterizing the spatial relationship of pixels in the HDAM high‐pressure zone, and for Endoflip, permutations of pressure and CSA combinations (ie, multiplication, division, or individually) at rest and squeeze were tested.ResultsIntramodality: (a) Endoflip: Best clustering was obtained using the combination of the ratio of CSA over pressure at 40 and 50 mL at rest, which had significantly better specificity (P < 0.001) than using only pressure at 50 mL, no difference in sensitivity (P = 0.68). (b) HDAM: clustering using textural information at rest had significantly higher specificity compared to using only the maximal pressure at rest (P < 0.001). Intermodality: Clustering results using optimal features were not significantly different with respect to sensitivity or specificity (P > 0.05). Optimal Endoflip feature set differed significantly in specificity compared to HDAM maximal pressure at both rest (P < 0.001) and squeeze (P < 0.001).ConclusionDefective anal closure function is fairly sensitive and highly specific in diagnosing FI. Using optimal feature sets, HDAM and Endoflip perform in a similar fashion in diagnosing FI, but are not complementary. High definition anal manometry and functional luminal imaging probe (endoflip) are two novel techniques to assess the anal closure function, however their sensitivity and specificity in diagnosing fecal incontinence (FI) is not clear. • We used machine learning tools to determine the ideal parameters provided by HDAM and endoflip; and performed blinded analysis on a group of FI patients and normal subjects to determine the sensitivity and specificity of two techniques in diagnosing FI. Our data show that both of these techniques are moderately sensitive but highly specific in the FI diagnosing; but they are not complimentary. How much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High-definition anorectal manometry (HDAM) and functional luminal imaging probe (Endoflip) are two new techniques to study anal sphincter function.BACKGROUNDHow much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High-definition anorectal manometry (HDAM) and functional luminal imaging probe (Endoflip) are two new techniques to study anal sphincter function.The goal was to compare the diagnostic utility of HDAM and Endoflip using optimal feature(s) in each modality for FI diagnosis.AIMSThe goal was to compare the diagnostic utility of HDAM and Endoflip using optimal feature(s) in each modality for FI diagnosis.Blinded classification was carried out on 70 female subjects (32 FI & 38 controls), using 3 prominent machine-learning clustering techniques, with 3 distance metrics. For HDAM, descriptive statistics, shape, and textural features characterizing the spatial relationship of pixels in the HDAM high-pressure zone, and for Endoflip, permutations of pressure and CSA combinations (ie, multiplication, division, or individually) at rest and squeeze were tested.METHODSBlinded classification was carried out on 70 female subjects (32 FI & 38 controls), using 3 prominent machine-learning clustering techniques, with 3 distance metrics. For HDAM, descriptive statistics, shape, and textural features characterizing the spatial relationship of pixels in the HDAM high-pressure zone, and for Endoflip, permutations of pressure and CSA combinations (ie, multiplication, division, or individually) at rest and squeeze were tested.Intramodality: (a) Endoflip: Best clustering was obtained using the combination of the ratio of CSA over pressure at 40 and 50 mL at rest, which had significantly better specificity (P < 0.001) than using only pressure at 50 mL, no difference in sensitivity (P = 0.68). (b) HDAM: clustering using textural information at rest had significantly higher specificity compared to using only the maximal pressure at rest (P < 0.001). Intermodality: Clustering results using optimal features were not significantly different with respect to sensitivity or specificity (P > 0.05). Optimal Endoflip feature set differed significantly in specificity compared to HDAM maximal pressure at both rest (P < 0.001) and squeeze (P < 0.001).RESULTSIntramodality: (a) Endoflip: Best clustering was obtained using the combination of the ratio of CSA over pressure at 40 and 50 mL at rest, which had significantly better specificity (P < 0.001) than using only pressure at 50 mL, no difference in sensitivity (P = 0.68). (b) HDAM: clustering using textural information at rest had significantly higher specificity compared to using only the maximal pressure at rest (P < 0.001). Intermodality: Clustering results using optimal features were not significantly different with respect to sensitivity or specificity (P > 0.05). Optimal Endoflip feature set differed significantly in specificity compared to HDAM maximal pressure at both rest (P < 0.001) and squeeze (P < 0.001).Defective anal closure function is fairly sensitive and highly specific in diagnosing FI. Using optimal feature sets, HDAM and Endoflip perform in a similar fashion in diagnosing FI, but are not complementary.CONCLUSIONDefective anal closure function is fairly sensitive and highly specific in diagnosing FI. Using optimal feature sets, HDAM and Endoflip perform in a similar fashion in diagnosing FI, but are not complementary. Background How much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High‐definition anorectal manometry (HDAM) and functional luminal imaging probe (Endoflip) are two new techniques to study anal sphincter function. Aims The goal was to compare the diagnostic utility of HDAM and Endoflip using optimal feature(s) in each modality for FI diagnosis. Methods: Blinded classification was carried out on 70 female subjects (32 FI & 38 controls), using 3 prominent machine‐learning clustering techniques, with 3 distance metrics. For HDAM, descriptive statistics, shape, and textural features characterizing the spatial relationship of pixels in the HDAM high‐pressure zone, and for Endoflip, permutations of pressure and CSA combinations (ie, multiplication, division, or individually) at rest and squeeze were tested. Results Intramodality: (a) Endoflip: Best clustering was obtained using the combination of the ratio of CSA over pressure at 40 and 50 mL at rest, which had significantly better specificity (P < 0.001) than using only pressure at 50 mL, no difference in sensitivity (P = 0.68). (b) HDAM: clustering using textural information at rest had significantly higher specificity compared to using only the maximal pressure at rest (P < 0.001). Intermodality: Clustering results using optimal features were not significantly different with respect to sensitivity or specificity (P > 0.05). Optimal Endoflip feature set differed significantly in specificity compared to HDAM maximal pressure at both rest (P < 0.001) and squeeze (P < 0.001). Conclusion Defective anal closure function is fairly sensitive and highly specific in diagnosing FI. Using optimal feature sets, HDAM and Endoflip perform in a similar fashion in diagnosing FI, but are not complementary. High‐definition anal manometry and functional luminal imaging probe (Endoflip) are two novel techniques to assess the anal closure function; however, their sensitivity and specificity in diagnosing fecal incontinence (FI) are not clear. We used machine‐learning tools to determine the ideal parameters provided by HDAM and Endoflip and performed blinded analysis on a group of FI patients and normal subjects to determine the sensitivity and specificity of two techniques in diagnosing FI. Our data show that both of these techniques are moderately sensitive but highly specific in the FI diagnosing, but they are not complimentary. How much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High-definition anorectal manometry (HDAM) and functional luminal imaging probe (Endoflip) are two new techniques to study anal sphincter function. The goal was to compare the diagnostic utility of HDAM and Endoflip using optimal feature(s) in each modality for FI diagnosis. Blinded classification was carried out on 70 female subjects (32 FI & 38 controls), using 3 prominent machine-learning clustering techniques, with 3 distance metrics. For HDAM, descriptive statistics, shape, and textural features characterizing the spatial relationship of pixels in the HDAM high-pressure zone, and for Endoflip, permutations of pressure and CSA combinations (ie, multiplication, division, or individually) at rest and squeeze were tested. Intramodality: (a) Endoflip: Best clustering was obtained using the combination of the ratio of CSA over pressure at 40 and 50 mL at rest, which had significantly better specificity (P < 0.001) than using only pressure at 50 mL, no difference in sensitivity (P = 0.68). (b) HDAM: clustering using textural information at rest had significantly higher specificity compared to using only the maximal pressure at rest (P < 0.001). Intermodality: Clustering results using optimal features were not significantly different with respect to sensitivity or specificity (P > 0.05). Optimal Endoflip feature set differed significantly in specificity compared to HDAM maximal pressure at both rest (P < 0.001) and squeeze (P < 0.001). Defective anal closure function is fairly sensitive and highly specific in diagnosing FI. Using optimal feature sets, HDAM and Endoflip perform in a similar fashion in diagnosing FI, but are not complementary. |
Author | Leroi, Anne M. Gourcerol, Guillaume Mittal, Ravinder K. Zifan, Ali Sun, Catherine |
AuthorAffiliation | 1 Department of Medicine, Division of Gastroenterology, University of California, LA Jolla, CA, USA 2 INSERM U1073, Service de Physiologie Digestive, CHU Rouen, INSERM CIC 1404 Rouen, F-76000 |
AuthorAffiliation_xml | – name: 2 INSERM U1073, Service de Physiologie Digestive, CHU Rouen, INSERM CIC 1404 Rouen, F-76000 – name: 1 Department of Medicine, Division of Gastroenterology, University of California, LA Jolla, CA, USA |
Author_xml | – sequence: 1 givenname: Ali surname: Zifan fullname: Zifan, Ali organization: University of California – sequence: 2 givenname: Catherine surname: Sun fullname: Sun, Catherine organization: University of California – sequence: 3 givenname: Guillaume surname: Gourcerol fullname: Gourcerol, Guillaume organization: CHU Rouen – sequence: 4 givenname: Anne M. orcidid: 0000-0002-6266-4203 surname: Leroi fullname: Leroi, Anne M. organization: CHU Rouen – sequence: 5 givenname: Ravinder K. surname: Mittal fullname: Mittal, Ravinder K. email: rmittal@ucsd.edu organization: University of California |
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CitedBy_id | crossref_primary_10_1016_j_gtc_2020_04_011 crossref_primary_10_1007_s11605_019_04237_6 crossref_primary_10_1111_nmo_14791 crossref_primary_10_1111_nmo_14496 crossref_primary_10_1111_nmo_13980 crossref_primary_10_1177_15533506211038088 crossref_primary_10_1007_s10620_020_06443_8 crossref_primary_10_5409_wjcp_v12_i4_162 crossref_primary_10_1038_s41551_022_00859_5 crossref_primary_10_1111_codi_15692 crossref_primary_10_1111_nmo_13972 crossref_primary_10_1111_nmo_14335 |
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Copyright | 2018 John Wiley & Sons Ltd 2018 John Wiley & Sons Ltd. Copyright © 2018 John Wiley & Sons Ltd Distributed under a Creative Commons Attribution 4.0 International License |
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Keywords | cluster analysis functional luminal imaging probe high-definition anorectal monetary unsupervised learning |
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Notes | Funding information This work was supported by a NIH Grant DK060733 and a grant from the Haute–Normandie Region in the framework of the Young Emerging Clinical Researchers call for projects, Rouen University Hospital. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 PMCID: PMC6249043 Author Contributions: AZ: Data analysis, figure preparation, and wrote the manuscript; CS: Data analysis, figure preparation; AML: Data Acquisition, data analysis; GG: Data Acquisition, data analysis; RKM: Data analysis, co-wrote the manuscript. |
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How much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High‐definition anorectal manometry (HDAM) and functional... How much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High-definition anorectal manometry (HDAM) and functional luminal... BackgroundHow much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High‐definition anorectal manometry (HDAM) and functional... High definition anal manometry and functional luminal imaging probe (endoflip) are two novel techniques to assess the anal closure function, however their... |
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SubjectTerms | Adult Anorectal cluster analysis Fecal incontinence Fecal Incontinence - diagnosis Female functional luminal imaging probe high‐definition anorectal monetary Human health and pathology Humans Imaging, Three-Dimensional - methods Learning algorithms Life Sciences Machine Learning Manometry - instrumentation Manometry - methods Middle Aged Pressure Sensitivity and Specificity Sphincter Tissues and Organs unsupervised learning Young Adult |
Title | Endoflip vs high‐definition manometry in the assessment of fecal incontinence: A data‐driven unsupervised comparison |
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