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This is a Human Clinical Trial study.

August 17, 2025Bioinformation

Artificial intelligence in diagnosis of maxillary sinusitis: A clinical study

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Clinical study shows AI achieves high sensitivity in diagnosing maxillary sinusitis, indicating potential for improved imaging accuracy.

AI model attained a sensitivity of 89.1%, enhancing diagnosis accuracy for maxillary sinusitis.
The overall accuracy rate for the AI diagnosis was 90.4%, demonstrating high reliability in clinical settings.
Employing cone-beam computed tomography, the study involved 200 patients with suspected maxillary sinusitis.
The findings suggest AI can effectively assist in interpreting imaging, improving consistency in diagnosis.

Manek et al. (2025) studied this question.

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August 17, 2025Bioinformation

View Full Paper

Ask AI

Bookmark

Authors

Clinical study shows AI achieves high sensitivity in diagnosing maxillary sinusitis, indicating potential for improved imaging accuracy.

AI model attained a sensitivity of 89.1%, enhancing diagnosis accuracy for maxillary sinusitis.
The overall accuracy rate for the AI diagnosis was 90.4%, demonstrating high reliability in clinical settings.
Employing cone-beam computed tomography, the study involved 200 patients with suspected maxillary sinusitis.
The findings suggest AI can effectively assist in interpreting imaging, improving consistency in diagnosis.

Manek et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark