Abstract: The disclosure discloses a multispectral imaging system and method for real time detection of breast cancer margin during a tumor resection surgery by combining terahertz and autofluorescence images. The system comprises terahertz and fluorescence modules in a housing having a sample holder to enable raster scan of the sample. The terahertz module has terahertz emitter antennas for generating terahertz radiation to the sample, and a terahertz detector to receive reflected terahertz signal from the sample. A fluorescence module with UV excitation LEDs induces fluorescence and a camera to receive emitted autofluorescence. A microcontroller connected to an electronic control unit is configured to display or to overlap and combine terahertz and autofluorescence images to determine breast cancer margin, via an AI module. The AI module is configured to perform classification of the feature set machine learning models to obtain classification with high with high sensitivity and specificity.

Abstract: Examples of assessing susceptibility of a target pathogen based on autofluorescence, are described. In an example, a sample containing a target pathogen and a predefined concentration of a target drug may be obtained. Thereafter, autofluorescence features based on autofluorescence exhibited by the sample in response to the sample being subjected to excitation radiation, may be determined. Once the autofluorescence features are determined, they may be analyzed based on a susceptibility-detection model to determine susceptibility of the target pathogen with respect to the target drug. In an example, the susceptibility-detection model is trained based on a training autofluorescence features correlated with reference data, in which the reference data includes information pertaining to susceptibility or resistance of a reference pathogen with respect to a reference drug.

Abstract: Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.

Abstract: Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.

Abstract: Device and methods for detection and classification of pathogens have an imaging module, an image processing module, and a display module. The imaging module has a plurality of light sources to expose a sample to excitation radiation at various wavelengths. A detector in the imaging module synchronously captures time-resolved fluorescence emission spectra, time-resolved reflectance, and transmittance spectra at multiple spectral bands from the sample. The image processing module resolves the spectra and compares obtained spectral parameters to set of standard parameters provided in a library database to determine a match to detect and classify pathogens.

Abstract: Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.

Abstract: Device and methods for detection and classification of pathogens have an imaging module, an image processing module, and a display module. The imaging module has a plurality of light sources to expose a sample to excitation radiation at various wavelengths. A detector in the imaging module synchronously captures time-resolved fluorescence emission spectra, time-resolved reflectance, and transmittance spectra at multiple spectral bands from the sample. The image processing module resolves the spectra and compares obtained spectral parameters to set of standard parameters provided in a library database to determine a match to detect and classify pathogens.