Tuberculose Pulmonar No Rio De Janeiro

Almeida, Thiago Tadeu Santos de; Boçon, Francisco; Carvalho, Igor Pereira de; Cortez, Igor Ferreira; Martins, Higno Rafael Machado; Fonseca, Marcilene Maria de Almeida; Fonsêca, Walter

doi:10.54620/cadesp.v16i2.625

Cited by 2 publications

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“…Consequently, this research proposes the utilization of Body Scan images for tuberculosis (TB) screening in prison environments 7 . The study is designed as a proof of concept, aiming to assess the feasibility of using Body Scan technology, commonly employed in Brazilian prison complexes for detecting objects like cell phones and drugs, as a potential tool for TB screening via artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence model in Body Scan images for monitoring tuberculosis in a prison complex

Deon,

Korb,

Santos

2024

JHS

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Tuberculosis (TB) remains a significant public health challenge worldwide, with its transmission exacerbated by various risk factors including co-existing health conditions and socio-economic determinants such as high population density, poverty, and alcoholism. This study emphasizes the crucial role of efficient TB screening and monitoring in not only providing prompt treatment to patients but also in reducing the disease's lethality. Responding to the Ministry of Health and the World Health Organization's demand for advanced diagnostic methods, we introduce a novel approach using the Marie. AI model for TB screening in penitentiary complexes. This proof of value (POV) repurposes Body Scan images, typically used for object detection on inmates, as a new tool for health screening. The Marie.AI model, a multimodal artificial intelligence platform developed in 2020, has previously proven effective in Brazil for aiding healthcare teams in diagnosing COVID-19 and TB. For this study, the model was trained on an extensive dataset of 1.5 million images, including X-rays and CT scans of patients with TB, COVID-19, and other pulmonary diseases, along with patient symptom data. A significant achievement of this study was the model's ability to distinguish between TB-infected and non-infected individuals using Body Scan images, leveraging its previous training with X-ray and CT data. The model demonstrated exceptional diagnostic accuracy, achieving a specificity of 87.23% and a sensitivity of 100% in identifying suspected TB cases. These findings not only highlight the versatility of the Marie.AI model in non-traditional settings but also mark a breakthrough in early TB diagnosis, particularly in high-risk environments like penitentiary complexes. This innovative approach promises to enhance public health responses to TB, leading to more effective disease management and control.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence model in Body Scan images for monitoring tuberculosis in a prison complex

Deon,

Korb,

Santos

2024

JHS

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“…The lack of materials and technologies for the diagnosis of TB in prison complexes encourages groups to develop applications in Public Health. Therefore, we propose using Body Scan images for TB screening in prison complexes 6 . This study, considered as a…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence model in Body Scan images for monitoring tuberculosis in a prison complex

Deon

Korb

Santos³

2022

Preprint

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Early diagnosis and monitoring of tuberculosis (TB) is of great value for disease control. In addition to clinical interpretation and laboratory tests, and also imaging tests such as x-ray. However, for this study we used the MARIE platform, as a support in prison complexes for monitoring and tracking TB, since the platform integrates more than 300 algorithms to identify different patterns in the images based on its dataset that contains around 1 million images. and thus providing better validation values for different image patterns. In this proof of concept we used Body Scan images to identify only suspected cases of TB. Of the three groups of images evaluated, we obtained high performance results when we simultaneously submitted images of the front and back of the lung. Sensitivity varies between 60%-100%. Marie represents an easy-to-use, end-to-end solution for identifying suspected TB in prison facilities.

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Tuberculose Pulmonar No Rio De Janeiro

Cited by 2 publications

References 0 publications

Artificial intelligence model in Body Scan images for monitoring tuberculosis in a prison complex

Artificial intelligence model in Body Scan images for monitoring tuberculosis in a prison complex

Artificial intelligence model in Body Scan images for monitoring tuberculosis in a prison complex

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