Artificial Intelligence-Based Patient Selection for Deep Inspiration Breath-Hold Breast Radiotherapy from Respiratory Signals

Artificial Intelligence-Based Patient Selection for Deep Inspiration Breath-Hold Breast Radiotherapy from Respiratory Signals

Purpose: to predict eligibility for deep inspiration breath-hold (DIBH) radiotherapy (RT) treatment of patients with left breast cancer from analysis of respiratory signal, using Deep Bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks. Methods: The respiratory traces from 36 pati...

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Bibliographic Details
Main Authors: Alessandra Vendrame, Cristina Cappelletto, Paola Chiovati, Lorenzo Vinante, Masud Parvej, Angela Caroli, Giovanni Pirrone, Loredana Barresi, Annalisa Drigo, Michele Avanzo
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Applied Sciences
Subjects:
deep learning
deep recurrent neural network (RNN)
bidirectional long short-term memory (BLSTM) neural network
radiotherapy
breast cancer
deep inspiration breath hold
Online Access:https://www.mdpi.com/2076-3417/13/8/4962

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https://www.mdpi.com/2076-3417/13/8/4962

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