Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

Real-Time Bronchoscope Localization Using Machine Learning To Improve Lung Cancer Diagnosis

Award Number: F30CA265234
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 09/01/2021
PERIOD OF PERFORMANCE END DATE: 05/31/2026

Group Awards By:

View Award Description

Real-Time Bronchoscope Localization Using Machine Learning To Improve Lung Cancer Diagnosis - Project Summary/Abstract Lung cancer 5-year survival rates drop from 61% for early stage diagnosis to just 6% for late stage diagnosis. Currently, fewer than 1 in 5 cases are diagnosed at an early stage. The increasing frequency of chest CT scans and changes in lung cancer screening guidelines are expected to increase the number of incidentally discovered lung lesions, representing an opportunity for earlier lung cancer diagnosis. Bronchoscopy is currently the safest, least invasive, and least expensive diagnostic option, but its poor diagnostic yield greatly limits its procedural beneﬁt. Even when advanced techniques like radial endobronchial ultrasound and electromagnetic navigation are used, the diagnostic yield is just 50-60%. This is primarily due to challenges with intraoperative localization of the bronchoscope prior to needle deployment. Additionally, access to these techniques is limited because they require expensive equipment and unique expertise. Efforts relying on the bronchoscope's built-in camera require no additional equipment or specialization, but have struggled with generalizability across individuals in part due to limited data availability and assumptions about airway features. The objective of this proposal is to improve the success rate of traditional bronchoscopes by addressing limita- tions in intraoperative localization using a data-driven model that is robust to differences in human anatomy. This work has potential for signiﬁcant public health beneﬁt by (1) increasing early lung cancer detection, (2) reducing morbidity and mortality by reducing the number of invasive procedures, and (3) making minimally invasive bron- choscopy more accessible in areas without expert bronchoscopists. The proposed work will be accomplished via two Speciﬁc Aims. In Aim 1, a dataset will be generated of virtual and real bronchoscopy videos with video-frame matched six degrees-of-freedom poses (position and orientation in three-dimensions) of the bronchoscope's dis- tal tip. This data will be made publicly available as the ﬁrst large dataset of its kind to promote future research and reproducibility. In Aim 2, a real-time bronchoscope localization model will be developed using advances in ma- chine learning, including deep neural networks, that have shown success in camera localization for non-medical applications. These models will regress the pose of the bronchoscope's distal tip using current and past video frames of the bronchoscope's built-in camera. The clinical utility of the system will be evaluated in simulation, 3D printed lung phantoms, and ex-vivo porcine lung experiments. The research, tightly coupled clinical experience, and associated training plan will provide a unique interdisciplinary skill-set in computer science, medical robotics, and procedural medicine. The outstanding research and clinical environment for this training at the University of North Carolina at Chapel Hill ensures exceptional preparation for a career conducting cutting-edge research as a physician-scientist in medical robotics.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $54,352 )
2025	2025	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Cancer Research Manpower	000	5	7/21/2025	NON-COMPETING CONTINUATION	$54,352
														Subtotal = $54,352

Issue Date FY: 2024 ( Subtotal = $53,788 )
2024	2024	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Cancer Research Manpower	000	4	7/22/2024	NON-COMPETING CONTINUATION	$53,788
														Subtotal = $53,788

Issue Date FY: 2023 ( Subtotal = $45,856 )
2023	2023	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Cancer Research Manpower	000	3	8/25/2023	NON-COMPETING CONTINUATION	$45,856
														Subtotal = $45,856

Issue Date FY: 2022 ( Subtotal = $38,262 )
2022	2022	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Cancer Research Manpower	000	2	8/4/2022	NON-COMPETING CONTINUATION	$38,262
														Subtotal = $38,262

Issue Date FY: 2021 ( Subtotal = $37,546 )
2021	2021	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Cancer Research Manpower	000	1	7/8/2021	NEW	$37,546
2021	2021	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Cancer Research Manpower	001	1	7/8/2021	NEW	$0
														Subtotal = $37,546

Grand Total All Awards = $229,804

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Real-Time Bronchoscope Localization Using Machine Learning To Improve Lung Cancer Diagnosis

Award Number: F30CA265234

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 09/01/2021

PERIOD OF PERFORMANCE END DATE: 05/31/2026

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer