Evaluación de Tubos de Recolección de Muestras de Sangre Utilizando Deep Learning

Ignacio Franco-Alucano; Julian Aguilar-Duque; Yolanda Baez-Lopez; Jorge Limon-Romero; María Marcela Solís-Quinteros; Diego Tlapa

doi:10.17488/RMIB.46.1.2

Autores/as

Ignacio Franco-Alucano Universidad Autónoma de Baja California, México https://orcid.org/0000-0002-9187-8251
Julian Aguilar-Duque Universidad Autónoma de Baja California, México
Yolanda Baez-Lopez Universidad Autónoma de Baja California, México https://orcid.org/0000-0001-8418-254X
Jorge Limon-Romero Universidad Autónoma de Baja California, México https://orcid.org/0000-0003-2117-4803
María Marcela Solís-Quinteros Universidad Autónoma de Baja California, México https://orcid.org/0000-0002-0567-0092
Diego Tlapa Universidad Autónoma de Baja California, México https://orcid.org/0000-0002-1491-5442

DOI:

https://doi.org/10.17488/RMIB.46.1.2

Palabras clave:

deep learning, detección de objetos, laboratorio clínico, muestra de sangre, red neuronal convolucional, YOLO

Resumen

La flebotomía es un procedimiento para obtener muestras de sangre principalmente para análisis clínicos en laboratorios. La cantidad de sangre, identificación de tubos y el uso del tubo adecuado son características que el profesional de la salud inspecciona visualmente. Al ser una actividad manual, la posibilidad de error está presente pudiendo tener efectos tanto en la calidad, como en el flujo de trabajo y eficiencia. A pesar del avance de las tecnologías de la industria 4.0, incluida la inteligencia artificial (IA), hay poca evidencia de aplicaciones en laboratorios clínicos. Este estudio tiene como objetivo evaluar la idoneidad de utilizar el aprendizaje profundo o deep learning (DL) en la inspección de tubos con muestras de sangre. Particularmente se prueban tres arquitecturas YOLOv5, YOLOv7 y YOLOv8 en la detección de seis clases incluyendo color de tapa y presencia de etiqueta. El mayor desempeño de precisión se presentó con el modelo YOLOv8 obteniendo una precisión de 0.927 en la detección, lo que evidencia una alta capacidad para inspeccionar características importantes en el servicio de flebotomía, siendo DL una alternativa viable para asistir a los profesionales de la salud en actividades de inspección. Trabajo futuro incluye ampliar el número de imágenes de manera balanceada.

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