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Elevating Patient Care with Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients

Elevating Patient Care with Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility… read more

Elevating Patient Care with Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients

Elevating Patient Care with Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility… read more

Elevating Patient Care with Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients

Elevating Patient Care with Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility… read more

Non-Contact Monitoring of Inhalation-Exhalation (I:E) Ratio in Non-Ventilated Subjects

Non-Contact Monitoring of Inhalation-Exhalation (I:E) Ratio in Non-Ventilated Subjects 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: The inhalation-exhalation (I:E) ratio, known to be an indicator of respiratory disease, is the ratio between the inhalation phase and exhalation phase of each breath. Here, we report on… read more

Non-Contact Monitoring of Inhalation-Exhalation (I:E) Ratio in Non-Ventilated Subjects

Non-Contact Monitoring of Inhalation-Exhalation (I:E) Ratio in Non-Ventilated Subjects 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: The inhalation-exhalation (I:E) ratio, known to be an indicator of respiratory disease, is the ratio between the inhalation phase and exhalation phase of each breath. Here, we report on… read more

A Multi-task based Deep Learning Framework with Landmark Detection For MRI Couinaud Segmentation

A Multi-task based Deep Learning Framework with Landmark Detection For MRI Couinaud Segmentation 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver function.… read more

A Multi-task based Deep Learning Framework with Landmark Detection For MRI Couinaud Segmentation

A Multi-task based Deep Learning Framework with Landmark Detection For MRI Couinaud Segmentation 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver function.… read more

A Multi-task based Deep Learning Framework with Landmark Detection For MRI Couinaud Segmentation

A Multi-task based Deep Learning Framework with Landmark Detection For MRI Couinaud Segmentation 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver function.… read more

A Multi-task based Deep Learning Framework with Landmark Detection For MRI Couinaud Segmentation

A Multi-task based Deep Learning Framework with Landmark Detection For MRI Couinaud Segmentation 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)
Objective: To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver function.… read more