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{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 40}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2021-05-16', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-06', 'completionDateStruct': {'date': '2021-11-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2022-03-31', 'studyFirstSubmitDate': '2021-05-25', 'studyFirstSubmitQcDate': '2021-05-25', 'lastUpdatePostDateStruct': {'date': '2022-04-01', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-05-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2021-05-16', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'ScanNav Anatomy PNB highlighting misidentification of structures', 'timeFrame': '6 months', 'description': 'Frequency of misidentification of structures \\[% of total, per anatomical region\\]'}], 'secondaryOutcomes': [{'measure': 'ScanNav Anatomy PNB highlighting identification of structures', 'timeFrame': '6 months', 'description': 'Frequency of correct identification of structures \\[% of total\\] and Frequency of non-identification of structures \\[% of total\\]'}, {'measure': 'ScanNav Anatomy PNB safety issues', 'timeFrame': '6 months', 'description': 'Frequency of safety issues \\[% of total\\]'}, {'measure': 'ScanNav Anatomy PNB adverse events', 'timeFrame': '6 months', 'description': 'Frequency of highlighting risking an adverse event \\[% of total\\]'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'conditions': ['Ultrasound Imaging of Anatomical Structures']}, 'referencesModule': {'references': [{'pmid': '35987706', 'type': 'DERIVED', 'citation': 'Bowness JS, Burckett-St Laurent D, Hernandez N, Keane PA, Lobo C, Margetts S, Moka E, Pawa A, Rosenblatt M, Sleep N, Taylor A, Woodworth G, Vasalauskaite A, Noble JA, Higham H. Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study. Br J Anaesth. 2023 Feb;130(2):217-225. doi: 10.1016/j.bja.2022.06.031. Epub 2022 Aug 18.'}]}, 'descriptionModule': {'briefSummary': 'This is a single-center, prospective study to be undertaken at University of Oregon, Portland, USA. The aim is to evaluate the clinical performance of ScanNav Anatomy PNB when highlighting anatomical structures during UGRA scanning. Specifically, we aim to assess and quantify the correct/incorrect highlighting of anatomical structures associated with ScanNav Anatomy PNB during UGRA scanning.', 'detailedDescription': 'Background:\n\nThe American Society of Regional Anesthesia and Pain Medicine (ASRA) has published evidence-based assessment of ultrasound-guided regional anesthesia (Neal et al., 2010) concluding that ultrasound guidance is superior or equal to other non-ultrasound nerve localization techniques. A subsequent publication from ASRA (Neal et al., 2016), has strengthened their position of ultrasound guidance being superior than other methods, including for the reduction of local anesthetic systemic toxicity. However, ultrasound-guided regional anesthesia (UGRA) is a difficult technique to master. A key activity of UGRA is ultrasound image interpretation (Sites et al., 2009) which ScanNav Anatomy PNB is designed to support. The data collected during this study will be assessed by a panel of intended users (experts in UGRA) to evaluate the performance and safety of ScanNav Anatomy PNB device highlighting.\n\nStatistical Methodology:\n\nValidation analyses will be conducted once the data collection has been complete. The collected scans will be processed, and the device output will be generated post hoc. Device output will be presented with raw ultrasound side-by-side. A panel of at least three expert anesthesiologists will review and evaluate each processed scan. The majority view of the panel will be used to evaluate each endpoint for any given structure ScanNav Anatomy PNB is intended to highlight.\n\nData collection and scan processing:\n\n40 different subjects will be scanned. The dataset will be balanced to contain approximately equal numbers of subjects with BMI\\<30 and BMI\\>= 30 kg/m2.\n\nAll data collection will be performed with FDA cleared general purpose ultrasound machine, ScanNav Anatomy PNB device will not be used during data collection.\n\nData characteristics for scan subjects (e.g., age and BMI) will be reported. Ultrasound scans for all 9 supported anatomical regions will be collected from both sides of each subject.\n\n90 x 10s clips per supported anatomical region will be generated, consisting of:\n\n* 80 x 10s scene clips: the block view (chosen by the expert scanner) together with the preceding 10 seconds of ultrasound scanning will be recorded (without the use of ScanNav Anatomy PNB)\n* 10 x 10s non-scene clips: 10 second ultrasound scans will be recorded at non-optimum block views, chosen by the expert scanner to represent plausible scanning errors (without the use of ScanNav Anatomy PNB) Scenes and non-scenes will be analyzed separately. Unmodified ultrasound video and highlighted video (color overlay produced by ScanNav Anatomy PNB generated post-hoc) will be presented side-by-side to independent experts for data analysis.\n\nData analysis:\n\nEvery clip will be presented to a minimum of 3 independent expert reviewers. All clips from a single anatomical region will be reviewed by the same 3 reviewers. Experts may review more than one anatomical region, but not necessarily all anatomical regions. Thus, a range of experts will review all anatomical regions.\n\nReviewers will be asked structured questions to assess the highlighting of safety critical anatomical structures (see definitions later in document) and the performance of ScanNav Anatomy PNB output for each individual clip.\n\nThe majority opinion (at least 2/3) will be obtained to establish the overall panel opinion (e.g., yes/yes/no = yes) for each structure on each clip.\n\nData will be evaluated and presented by structure in each anatomical region and overall (i.e., total for each class; nerve, artery etc.).\n\nInter-rater agreement between the reviewers will be reported on an anatomical region basis.\n\nData will be presented as frequencies and presented as percent of total clips analyzed.\n\nData analysis will include stratification by subject age, BMI, and ultrasound machine to ensure consistency across these variables.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '60 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Potential study subjects will be healthy volunteers, chosen to provide a variety of ages and BMIs. Subjects will be screened to ensure approximate equal numbers in coverage for BMI (\\<30, 30-39) category.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Male or female, at least 18 years of age;\n* Able to comprehend and sign the Informed Consent prior to enrolment in the study.\n* Vaccinated against SARS-CoV-2\n\nExclusion Criteria:\n\n* Aged \\<18 years of age;\n* Unwilling or unable to provide informed consent.\n* BMI\\> 39 kg/m2\n* Known pathology of the area to be scanned'}, 'identificationModule': {'nctId': 'NCT04906018', 'briefTitle': "A Study to Collect Imaging Data for the Validation of the Intelligent Ultrasound's ScanNav Anatomy Peripheral Nerve Block (PNB) - US v1.0", 'organization': {'class': 'INDUSTRY', 'fullName': 'IntelligentUltrasound Limited'}, 'officialTitle': "A Study to Collect Imaging Data for the Validation of the Intelligent Ultrasound's ScanNav Anatomy Peripheral Nerve Block (PNB) - US v1.0", 'orgStudyIdInfo': {'id': 'IU2021_AG_07'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'volunteer group - BMI less than 30', 'description': 'Each subject with a BMI less than 30 will be randomly allocated to an ultrasound machine and an expert scanner who will perform the ultrasound scans of all supported anatomical regions.', 'interventionNames': ['Device: Ultrasound scans']}, {'label': 'volunteer group - BMI of 30 and above', 'description': 'Each subject with a BMI of 30 and above will be randomly allocated to an ultrasound machine and an expert scanner who will perform the ultrasound scans of all supported anatomical regions.', 'interventionNames': ['Device: Ultrasound scans']}], 'interventions': [{'name': 'Ultrasound scans', 'type': 'DEVICE', 'description': 'Each subject will be randomly allocated to an ultrasound machine and an expert scanner who will perform the ultrasound scans of all supported anatomical regions.', 'armGroupLabels': ['volunteer group - BMI less than 30', 'volunteer group - BMI of 30 and above']}]}, 'contactsLocationsModule': {'locations': [{'zip': '97239', 'city': 'Portland', 'state': 'Oregon', 'country': 'United States', 'facility': 'Oregon Health & Science University', 'geoPoint': {'lat': 45.52345, 'lon': -122.67621}}], 'overallOfficials': [{'name': 'Glenn Woodworth, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Oregon Health and Science University'}, {'name': 'James Bowness, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of Oxford & Royal Gwent Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'IntelligentUltrasound Limited', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}