Public articles linked to the same research event.
Journal of Infection and Chemotherapy This pilot study collected ticks between May 2023 and December 2024 from patients presenting tick bites at 10 medical institutions in Okayama, Hiroshima, and Kagawa prefectures, supplemented with wild tick images, and developed a two-stage AI pipeline of object detection and genus-level classification in which a YOLO-based model trained on 3258 annotated public images achieved mAP@0.5 of 0.954 and ResNet50 achieved mean validation accuracy of 95% on 533 tick images covering four genera, integrating the system into a prototype smartphone application with geospatial visualization to support clinical risk assessment and public health surveillance.
This pilot study collected ticks between May 2023 and December 2024 from patients presenting tick bites at 10 medical institutions in Okayama, Hiroshima, and Kagawa prefectures, supplemented with wild tick images, and developed a two-stage AI pipeline of object detection and genus-level classification in which a YOLO-based model trained on 3258 annotated public images achieved mAP@0.5 of 0.954 and ResNet50 achieved mean validation accuracy of 95% on 533 tick images covering four genera, integrating the system into a prototype smartphone application with geospatial visualization to support clinical risk assessment and public health surveillance.
This pilot study collected ticks between May 2023 and December 2024 from patients presenting tick bites at 10 medical institutions in Okayama, Hiroshima, and Kagawa prefectures, supplemented with wild tick images, and developed a two-stage AI pipeline of object detection and genus-level classification in which a YOLO-based model trained on 3258 annotated public images achieved mAP@0.5 of 0.954 and ResNet50 achieved mean validation accuracy of 95% on 533 tick images covering four genera, integrating the system into a prototype smartphone application with geospatial visualization to support clinical risk assessment and public health surveillance.
This pilot study collected ticks between May 2023 and December 2024 from patients presenting tick bites at 10 medical institutions in Okayama, Hiroshima, and Kagawa prefectures, supplemented with wild tick images, and developed a two-stage AI pipeline of object detection and genus-level classification in which a YOLO-based model trained on 3258 annotated public images achieved mAP@0.5 of 0.954 and ResNet50 achieved mean validation accuracy of 95% on 533 tick images covering four genera, integrating the system into a prototype smartphone application with geospatial visualization to support clinical risk assessment and public health surveillance.