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505
RECIMUNDO VOL. 7 N°1 (2023)
2013). La mayor tasa de complicaciones de
la esofagectomía es intrínseca a la técnica
de reconstrucción esofágica, que tradicio-
nalmente involucra un injerto de estómago,
es decir, el conducto gástrico.
Conclusión
La imagen hiperespectral es potencialmente
una herramienta valiosa para la guía intraope-
ratoria porque permite realizar una biopsia
óptica no destructiva y sin contraste, anali-
zando la composición química del tejido a ni-
vel molecular y prácticamente en tiempo real.
La mayor parte de la investigación preclínica
se ha realizado en una evaluación de perfu-
sión intraoperatoria basada en HSI, que vali-
dó este tipo de aplicación en varios órganos,
principalmente utilizando configuraciones
experimentales sólidas. Además, la cuantifi-
cación de la oxigenación tisular es una infor-
mación bien conocida y de fácil acceso para
extraer del hipercubo, que no requiere la ayu-
da de complejos algoritmos de aprendizaje
automático. Por ello, hoy en día esta aplica-
ción representa el uso de HSI más factible
en la práctica clínica, tal y como lo confirma
la existencia de numerosos estudios clínicos
entre diversas disciplinas quirúrgicas.
No obstante, el reconocimiento de tejidos
basado en HSI aún se encuentra en la eta-
pa de investigación y desarrollo. Por ello, su
uso fuera de los protocolos de investigación
aún se mantiene alejado de la práctica clí-
nica habitual. Existen inconvenientes con-
sistentes comunes a todas las aplicaciones
HSI intraoperatorias, lo que limita la difusión
de esta tecnología en el quirófano. De he-
cho, los sistemas de imágenes hiperespec-
trales disponibles todavía son relativamente
voluminosos y no son adecuados para la
cirugía mínimamente invasiva.
Finalmente se debe reconocer el potencial
de esta modalidad de imágenes en el cam-
po quirúrgico por lo cual varios grupo de
investigadores se han enfocado en superar
las limitaciones de HSI en todo el mundo,
en busca de un sistema HSI de video minia-
turizado que combine la cuantificación de
la perfusión y algoritmos específicos de re-
conocimiento automático de tejidos en aras
de integrarse como parte del quirófano de
cirugía de precisión.
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