Dataset Information

Development of a Detection Algorithm for Use with Reflectance-Based, Real-Time Chemical Sensing.

ABSTRACT: Here, we describe our efforts focused on development of an algorithm for identification of detection events in a real-time sensing application relying on reporting of color values using commercially available color sensing chips. The effort focuses on the identification of event occurrence, rather than target identification, and utilizes approaches suitable to onboard device incorporation to facilitate portable and autonomous use. The described algorithm first excludes electronic noise generated by the sensor system and determines response thresholds. This automatic adjustment provides the potential for use with device variations as well as accommodating differing indicator behaviors. Multiple signal channels (RGB) as well as multiple indicator array elements are combined for reporting of an event with a minimum of false responses. While the method reported was developed for use with paper-supported porphyrin and metalloporphyrin indicators, it should be equally applicable to other colorimetric indicators. Depending on device configurations, receiver operating characteristic (ROC) sensitivities of 1 could be obtained with specificities of 0.87 (threshold 160 ppb, ethanol).

SUBMITTER: Malanoski AP

PROVIDER: S-EPMC5134586 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Development of a Detection Algorithm for Use with Reflectance-Based, Real-Time Chemical Sensing.

Malanoski Anthony P AP Johnson Brandy J BJ Erickson Jeffrey S JS Stenger David A DA

Sensors (Basel, Switzerland) 20161116 11

Here, we describe our efforts focused on development of an algorithm for identification of detection events in a real-time sensing application relying on reporting of color values using commercially available color sensing chips. The effort focuses on the identification of event occurrence, rather than target identification, and utilizes approaches suitable to onboard device incorporation to facilitate portable and autonomous use. The described algorithm first excludes electronic noise generated ...[more]

PMID: 27854335

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Project description:BACKGROUND:Breast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery. We report on the feasibility of using diffuse reflectance spectroscopy (DRS) for real-time in vivo tissue characterization. METHODS:Evaluating feasibility of the technology requires a setting in which measurements, imaging and pathology have the best possible correlation. For this purpose an optical biopsy needle was used that had integrated optical fibers at the tip of the needle. This approach enabled the best possible correlation between optical measurement volume and tissue histology. With this optical biopsy needle we acquired real-time DRS data of normal tissue and tumor tissue in 27 patients that underwent an ultrasound guided breast biopsy procedure. Five additional patients were measured in continuous mode in which we obtained DRS measurements along the entire biopsy needle trajectory. We developed and compared three different support vector machine based classification models to classify the DRS measurements. RESULTS:With DRS malignant tissue could be discriminated from healthy tissue. The classification model that was based on eight selected wavelengths had the highest accuracy and Matthews Correlation Coefficient (MCC) of 0.93 and 0.87, respectively. In three patients that were measured in continuous mode and had malignant tissue in their biopsy specimen, a clear transition was seen in the classified DRS measurements going from healthy tissue to tumor tissue. This transition was not seen in the other two continuously measured patients that had benign tissue in their biopsy specimen. CONCLUSIONS:It was concluded that DRS is feasible for integration in a surgical tool that could assist the breast surgeon in detecting positive resection margins during breast surgery. Trail registration NIH US National Library of Medicine-clinicaltrails.gov, NCT01730365. Registered: 10/04/2012 https://clinicaltrials.gov/ct2/show/study/NCT01730365.

Dataset Information

Development of a Detection Algorithm for Use with Reflectance-Based, Real-Time Chemical Sensing.

Publications

Development of a Detection Algorithm for Use with Reflectance-Based, Real-Time Chemical Sensing.

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