GreenPrints Institutional repository of De La Salle Medical and Health Sciences Institut
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Artwork 117: Golden Rays
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Artwork 113: Harvest
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Artwork 027: The Night Takes Rook
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Angel with Harp
No full text2122-RPA-311https://greenprints.dlshsi.edu.ph/collections_3d/1067/thumbnail.jp
Nativity Set 1
No full text2122-RPA-315https://greenprints.dlshsi.edu.ph/collections_3d/1072/thumbnail.jp
Bangkok declarations on cancer control in Asia
No full textThe “Bangkok Declarations” highlight key strategies to reduce the cancer burden in Asia. Recognizing the role of lifestyle factors like tobacco use, unhealthy diets, and inactivity, they advocate for public health initiatives promoting healthier communities. Environmental risks must also be addressed to enhance prevention strategies. Vaccination programs targeting cancer-causing viruses are emphasized for reducing incidence rates. However, limited access to cancer screening and early detection services remains a challenge, requiring improvements to boost early intervention and patient outcomes. The financial burden of cancer treatment is critical, necessitating cost-effective therapies and financial support systems. Mental health support for patients and caregivers is essential for holistic care. Digital health technologies and AI offer promise for improving diagnosis and treatment in resource-limited settings. Collaboration among stakeholders is vital, with culturally sensitive approaches ensuring equitable care. Research should focus on lifestyle, environmental factors, innovative prevention, and access to care for effective cancer control
Accelerating Cough-Based Algorithms for Pulmonary Tuberculosis Screening: Results From the CODA TB DREAM Challenge
No full textBackground Open-access data challenges can accelerate innovation in artificial intelligence-based tools. In the Cough Diagnostic Algorithm for Tuberculosis (CODA TB) DREAM Challenge, we developed and independently validated cough sound-based artificial intelligence algorithms for tuberculosis screening. Methods We included data from 2143 adults with ≥2 weeks of cough from outpatient clinics in India, Madagascar, the Philippines, South Africa, Tanzania, Uganda, and Vietnam. A standard tuberculosis evaluation was completed, and ≥3 solicited coughs were recorded using a smartphone. We invited teams to develop models using training data to classify microbiologically confirmed tuberculosis disease using (1) cough sound features only and/or (2) cough sound features with routinely available clinical data. After 4 months, they submitted the algorithms for independent test set validation. Models were ranked by area under the receiver operating characteristic curve (AUROC) and partial AUROC (pAUROC) to achieve at least 80% sensitivity and 60% specificity. Results Eleven cough models and 6 cough-plus-clinical models were submitted. AUROCs for cough models ranged from 0.69 to 0.74, and the highest performing model achieved 55.5% specificity (95% confidence interval, 47.7%-64.2%) at 80% sensitivity. The addition of clinical data improved AUROCs (range, 0.78-0.83); 5 of the 6 models reached the target pAUROC, and the highest performing model had 73.8% specificity (95% confidence interval, 60.8%-80.0%) at 80% sensitivity. The AUROC varied by country and was higher among male and human immunodeficiency virus-negative individuals. Conclusions In a short period, an open-access data challenge facilitated the development of new cough-based tuberculosis algorithms and demonstrated potential as a tuberculosis screening tool