Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D011014', 'term': 'Pneumonia'}, {'id': 'D001424', 'term': 'Bacterial Infections'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D012141', 'term': 'Respiratory Tract Infections'}, {'id': 'D007239', 'term': 'Infections'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D001423', 'term': 'Bacterial Infections and Mycoses'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 100}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2025-08-31', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-07', 'completionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-03', 'studyFirstSubmitDate': '2025-08-03', 'studyFirstSubmitQcDate': '2025-08-03', 'lastUpdatePostDateStruct': {'date': '2025-08-11', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-08-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-08-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Comparing the diagnostic accuracy of AI-ROSE with traditional bronchoalveolar lavage fluid examination methods.', 'timeFrame': '2026-3-31'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Severe Pneumonia', 'artificial intelligence', 'Rapid on-site evaluation', 'Diagnosis'], 'conditions': ['Severe Pneumonia', 'Bacterial Infections']}, 'descriptionModule': {'briefSummary': 'AI-ROSE is an innovative immunofluorescence staining combined with artificial intelligence image analysis technology that uses a fully automated fluorescence microscope to image pathogens in real time. AI algorithms automatically identify pathogen types (such as bacteria, fungi, etc.) and cellular backgrounds, quickly interpret results, and automatically issue color graphic reports for clinical doctors to take earlier and more accurate targeted treatment for critically ill patients. This study used bronchoalveolar lavage fluid as a biological sample and compared it with traditional examination methods to analyze the diagnostic accuracy and clinical practicality of AI-ROSE in patients with severe pneumonia.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Infected severe pneumonia patients', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age 18 years and above\n* The preliminary clinical diagnosis is a patient with lower respiratory tract infection\n* Meets the criteria for severe pneumonia, which means one of the following conditions exists: ① Main criteria: septic shock requiring vasoactive drug support; Respiratory failure requiring mechanical ventilation; ② Secondary criteria: Meet at least 3 or more criteria, including respiratory rate\\>30 times/minute; Oxygenation index (PaO ₂/FiO ₂)\\<250; Multiple leaf segment infiltration; Consciousness disorders/orientation disorders; Urea nitrogen level\\>20mg/dL; White blood cell count\\<4 \\* 10 \\^ 9/L; platelet count\\<100 \\* 10 \\^ 9/L; core body temperature\\<36.8 ° C; hypotension requiring active fluid resuscitation\n* The patient agrees to undergo bronchoscopy and bronchoalveolar lavage, and agrees to perform AI-ROSE testing on the bronchoalveolar lavage fluid\n\nExclusion Criteria:\n\n* Patients with poor basic condition, severe illness, and inability to tolerate bronchoscopy examination\n* The bronchoalveolar lavage fluid sample is not qualified'}, 'identificationModule': {'nctId': 'NCT07113951', 'briefTitle': 'Diagnostic Application of AI-ROSE in Severe Pneumonia', 'organization': {'class': 'OTHER', 'fullName': 'The First Affiliated Hospital with Nanjing Medical University'}, 'officialTitle': 'Diagnostic Application of Microbiological Fluorescence Rapid On-site Evaluation Combined With Artificial Intelligence in Severe Pneumonia: a Multi-center Prospective Study', 'orgStudyIdInfo': {'id': '2025-SR-567'}}, 'contactsLocationsModule': {'locations': [{'zip': '210036', 'city': 'Nanjing', 'state': 'Jiangsu', 'country': 'China', 'facility': 'Fluorescence Biological Microscope', 'geoPoint': {'lat': 32.06167, 'lon': 118.77778}}], 'overallOfficials': [{'name': 'Wenkui Sun', 'role': 'STUDY_DIRECTOR', 'affiliation': 'First Affiliated Hospital of Nanjing Medical University Nanjing'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The First Affiliated Hospital with Nanjing Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}