Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002972', 'term': 'Cleft Palate'}, {'id': 'D002971', 'term': 'Cleft Lip'}], 'ancestors': [{'id': 'D007569', 'term': 'Jaw Abnormalities'}, {'id': 'D007571', 'term': 'Jaw Diseases'}, {'id': 'D009140', 'term': 'Musculoskeletal Diseases'}, {'id': 'D019767', 'term': 'Maxillofacial Abnormalities'}, {'id': 'D019465', 'term': 'Craniofacial Abnormalities'}, {'id': 'D009139', 'term': 'Musculoskeletal Abnormalities'}, {'id': 'D009057', 'term': 'Stomatognathic Diseases'}, {'id': 'D009056', 'term': 'Mouth Abnormalities'}, {'id': 'D009059', 'term': 'Mouth Diseases'}, {'id': 'D018640', 'term': 'Stomatognathic System Abnormalities'}, {'id': 'D000013', 'term': 'Congenital Abnormalities'}, {'id': 'D009358', 'term': 'Congenital, Hereditary, and Neonatal Diseases and Abnormalities'}, {'id': 'D008047', 'term': 'Lip Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 450}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2020-03-05', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-03', 'completionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-03-19', 'studyFirstSubmitDate': '2020-04-08', 'studyFirstSubmitQcDate': '2020-04-08', 'lastUpdatePostDateStruct': {'date': '2025-03-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-04-10', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': '3D palatal geometry', 'timeFrame': 'at baseline', 'description': 'palatal cleft photographs (input) and corresponding 3D palatal geometry (output) create this 3D palatal geometry for development of image based, non-invasive palatal shape reconstruction'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Cleft Lip', 'Neural Network', 'palatal plate therapy', 'palatal 3D geometry', 'palatal shape reconstruction'], 'conditions': ['Cleft Palate', 'Orofacial Cleft']}, 'descriptionModule': {'briefSummary': 'This study is to develop a neural network to compute palatal three dimensional (3D) geometry by using routinely taken intraoral/palatal photographs and palatal casts of infants with cleft lip and palate deformity for reducing cleft lip and palate treatment burden. Data of palatal casts and palatal images of cleft patients routinely treated at the University Hospital Basel will be analyzed.The collection of large data helps in developing a neural network that will allow the computation of the 3D geometry from single photographs.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with cleft lip and palate malformation with available routinely performed plaster cast model from palatal impressions and/or intraoral scans of palate, and corresponding images of the cleft palate treated in the past as well as future patients treated at University hospital Basel (USB) for the last 50 years will be included (1970 till 2025). Models were (until 2020) and intraoral scans are (since 2020) taken routinely in newborns and for follow-up (age 1-20 years) according to WHO Guidelines.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients with cleft lip and palate malformation with available routinely performed plaster cast model from palatal impressions, intraoral scans from the palate and corresponding images of the cleft palate (1970 till 2025)\n\nExclusion Criteria:\n\n* Existence of a documented rejection'}, 'identificationModule': {'nctId': 'NCT04342234', 'briefTitle': 'Neural Network to Calculate Morphology of the Cleft Palate to Reduce Cleft Lip and Palate Treatment Burden.', 'organization': {'class': 'OTHER', 'fullName': 'University Hospital, Basel, Switzerland'}, 'officialTitle': 'Neural Network to Calculate Morphology of the Cleft Palate to Reduce Cleft Lip and Palate Treatment Burden.', 'orgStudyIdInfo': {'id': '2020-00388; ch19Mueller2'}}, 'armsInterventionsModule': {'interventions': [{'name': 'data collection of palatal casts and palatal images of cleft patients routinely treated at the institution', 'type': 'OTHER', 'description': 'data collection of palatal casts and palatal images of cleft patients, using routinely taken intraoral/palatal photographs and palatal casts of infants with cleft lip and palate deformity'}]}, 'contactsLocationsModule': {'locations': [{'zip': '4031', 'city': 'Basel', 'status': 'RECRUITING', 'country': 'Switzerland', 'contacts': [{'name': 'Benito Benitez, Dr. med. Dr. med. dent.', 'role': 'CONTACT', 'email': 'benito.benitez@usb.ch', 'phone': '+41 61 556 52 85'}, {'name': 'Andreas Müller, PD Dr. med. Dr. med. dent.', 'role': 'CONTACT', 'email': 'andreas.mueller@usb.ch', 'phone': '+41 061 328 60 95'}, {'name': 'Martin Erismann', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Prasad Nalabothu, Dr.', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Mund-, Kiefer- und Gesichtschirurgie, Universitätsspital Basel', 'geoPoint': {'lat': 47.55839, 'lon': 7.57327}}], 'centralContacts': [{'name': 'Benito Benitez, Dr. med. Dr. med. dent.', 'role': 'CONTACT', 'email': 'benito.benitez@usb.ch', 'phone': '+41 61 556 52 85'}, {'name': 'Andreas Müller, PD Dr. med. Dr. med. dent. Dr.', 'role': 'CONTACT', 'email': 'andreas.mueller@usb.ch', 'phone': '+41 061 328 60 95'}], 'overallOfficials': [{'name': 'Andreas Müller, PD Dr. med. Dr. med. dent. Dr.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Mund-, Kiefer- und Gesichtschirurgie, Universitätsspital Basel'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University Hospital, Basel, Switzerland', 'class': 'OTHER'}, 'collaborators': [{'name': 'Botnar Research Centre for Child Health (BRCCH)', 'class': 'UNKNOWN'}, {'name': 'sciCORE Basel', 'class': 'UNKNOWN'}, {'name': 'Department of Computer Science, Computer Graphics Laboratory, ETH Zurich', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}