Detection of In vivo Oral Epithelial Cancer using Fluorescence Lifetime Imaging

Endogenous fluorescence lifetime imaging microscopy (FLIM) provides a nondestructive means to interrogate the biochemical composition of biological tissues. Therefore, it has the potential to identify tissue pre-malignant and malignant transformation. In this study, we evaluate the potential of endogenous FLIM for detecting benign oral lesions from pre-malignant and malignant oral lesions. Using a database of FLIM images (n=20) obtained in vivo from the oral cavity of patients undergoing tissue biopsy, we were able to identify specific features from the characteristic FLIM signal of benign, mild dysplastic, and cancerous oral lesions. These features were used to train statistical classification rules aimed to detect benign lesions from either dysplastic and cancerous lesions. Our results indicated that dysplastic and cancerous lesions could be detected from benign lesions with sensitivity of ~89% and specificity of ~95%. Our future efforts are focused on further developing our classification algorithms with additional FLIM in vivo data

Detection of In vivo Oral Epithelial Cancer using Fluorescence Lifetime Imaging

Endogenous fluorescence lifetime imaging microscopy (FLIM) provides a nondestructive means to interrogate the biochemical composition of biological tissues. Therefore, it has the potential to identify tissue pre-malignant and malignant transformation. In this study, we evaluate the potential of endogenous FLIM for detecting benign oral lesions from pre-malignant and malignant oral lesions. Using a database of FLIM images (n=20) obtained in vivo from the oral cavity of patients undergoing tissue biopsy, we were able to identify specific features from the characteristic FLIM signal of benign, mild dysplastic, and cancerous oral lesions. These features were used to train statistical classification rules aimed to detect benign lesions from either dysplastic and cancerous lesions. Our results indicated that dysplastic and cancerous lesions could be detected from benign lesions with sensitivity of ~89% and specificity of ~95%. Our future efforts are focused on further developing our classification algorithms with additional FLIM in vivo data

Detection of In vivo Oral Epithelial Cancer using Fluorescence Lifetime Imaging

Endogenous fluorescence lifetime imaging microscopy (FLIM) provides a nondestructive means to interrogate the biochemical composition of biological tissues. Therefore, it has the potential to identify tissue pre-malignant and malignant transformation. In this study, we evaluate the potential of endogenous FLIM for detecting benign oral lesions from pre-malignant and malignant oral lesions. Using a database of FLIM images (n=20) obtained in vivo from the oral cavity of patients undergoing tissue biopsy, we were able to identify specific features from the characteristic FLIM signal of benign, mild dysplastic, and cancerous oral lesions. These features were used to train statistical classification rules aimed to detect benign lesions from either dysplastic and cancerous lesions. Our results indicated that dysplastic and cancerous lesions could be detected from benign lesions with sensitivity of ~89% and specificity of ~95%. Our future efforts are focused on further developing our classification algorithms with additional FLIM in vivo data

Detection of In vivo Oral Epithelial Cancer using Fluorescence Lifetime Imaging

Endogenous fluorescence lifetime imaging microscopy (FLIM) provides a nondestructive means to interrogate the biochemical composition of biological tissues. Therefore, it has the potential to identify tissue pre-malignant and malignant transformation. In this study, we evaluate the potential of endogenous FLIM for detecting benign oral lesions from pre-malignant and malignant oral lesions. Using a database of FLIM images (n=20) obtained in vivo from the oral cavity of patients undergoing tissue biopsy, we were able to identify specific features from the characteristic FLIM signal of benign, mild dysplastic, and cancerous oral lesions. These features were used to train statistical classification rules aimed to detect benign lesions from either dysplastic and cancerous lesions. Our results indicated that dysplastic and cancerous lesions could be detected from benign lesions with sensitivity of ~89% and specificity of ~95%. Our future efforts are focused on further developing our classification algorithms with additional FLIM in vivo data

Process Algebraic Approach to the Schedulability Analysis and Workload Abstraction of Hierarchical Real-Time Systems

Real-time embedded systems have increased in complexity. As microprocessors become more powerful, the software complexity of real-time embedded systems has increased steadily. The requirements for increased functionality and adaptability make the development of real-time embedded software complex and error-prone. Component-based design has been widely accepted as a compositional approach to facilitate the design of complex systems. It provides a means for decomposing a complex system into simpler subsystems and composing the subsystems in a hierarchical manner. A system composed of real-time subsystems with hierarchy is called a hierarchical real-time system This paper describes a process algebraic approach to schedulability analysis of hierarchical real-time systems. To facilitate modeling and analyzing hierarchical real-time systems, we conservatively extend an existing process algebraic theory based on ACSR-VP (Algebra of Communicating Shared Resources with Value-Passing) for the schedulability of real-time systems. We explain a method to model a resource model in ACSR-VP which may be partitioned for a subsystem. We also introduce schedulability relation to define the schedulability of hierarchical real-time systems and show that satisfaction checking of the relation is reducible to deadlock checking in ACSR-VP and can be done automatically by the tool support of ERSA (Verification, Execution and Rewrite System for ACSR). With the schedulability relation, we present algorithms for abstracting real-time system workloads