se of Artificial Intelligence (AI) for identification of Pulmonary Tuberculosis using chest ultrasound videos
- Conditions
- Health Condition 1: A150- Tuberculosis of lung
- Registration Number
- CTRI/2022/04/041711
- Lead Sponsor
- ational Entrepreneurship Network NEN Artificial Intelligence Unit
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- ot Yet Recruiting
- Sex
- Not specified
- Target Recruitment
- 0
1. All subjects consenting to be a part of the study. Individual consent (signed and dated informed consent form) 2. 18 years and above of age 3A. For category of TB subjects, include those satisfying the following criteria â?? (a) Patients should have had any of the following (one or more) symptoms of pulmonary tuberculosis as identified by the clinician at the time of presentation - Persistent cough for 2 weeks or more; Night sweats; Chest pain; Weight loss(unintentional); Shortness of breath; Feeling tired or weak; Fever-Body temperature of more than 100.4 degrees Fahrenheit (CDC) (b)Patients X-Ray chest showing findings suggestive of tuberculosis.(c)Additionally, patients should be Microbiologically confirmed (sputum microscopy/ CBNAAT/ TruNat) OR Clinically diagnosed TB Cases (d) Clinically stable individuals - Individuals that do not require emergency medical attention. 3B. For category of Non-TB subjects, include those satisfying the following criteria â?? (a) In the Chest symptomatic category: Subjects having symptoms of other chest conditions/pathologies. Chest x-ray findings ruling out pulmonary tuberculosis but may have other findings.(b) In Normal subjectsâ?? category: Subjects should be clinically stable individuals
with no symptoms suggestive of pulmonary conditions. Chest x-ray findings should indicate a clear chest with no lesions
1. Consent not given by the individual for enrolment in the study. 2. In the TB positive subjects, exclude all clinically unstable individuals.
3. For Non-TB subjects, in the Normal individualâ??s category, exclude all clinically ill individuals
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Creating a dataset of Chest X-rays, HRCT and Chest Ultrasound Scans (CUS) of adult subjects.Timepoint: 1 year
- Secondary Outcome Measures
Name Time Method 1. To use this structured dataset to develop an AI-powered screening tool for triaging pulmonary tuberculosis patients from the community <br/ ><br>2. To anonymize the dataset by removing all Personal Identifiable Information (PII) and make it publicly available for the global research community. <br/ ><br>Timepoint: 1 year