Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: In alternative embodiments, provided are immunohistochemistry (IHC) methods for determining and scoring reproducibly the extent of expression of the protein carcinoembryonic antigen-related cell adhesion molecule 5, or CEACAM5, in a tissue sample. In alternative embodiments, provided are methods for diagnosing, treating or ameliorating or assessing the risk of recurrence for a cancer or a tumor using an IHC method as provided herein. In alternative embodiments, provided are kits comprising components and instructions for practicing methods as provided herein. The present application describes methods for scoring CEACAM5 expression and utilizing the score as a companion or complementary diagnostic or to treat or ameliorate cancer or a tumor.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Certain aspects relate to apparatuses and techniques for non-invasive and non-contact optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications including assessing the presence and severity of tissue conditions, such as necrosis and small vessel disease, at a potential or determined amputation site.

Abstract: Certain aspects relate to apparatuses and techniques for non-invasive and non-contact optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications including assessing the presence and severity of tissue conditions, such as necrosis and small vessel disease, at a potential or determined amputation site.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: Certain aspects relate to apparatuses and techniques for non-invasive optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds.

Abstract: Certain aspects relate to apparatuses and techniques for non-invasive optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Certain aspects relate to apparatuses and techniques for non-invasive and non-contact optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications including assessing the presence and severity of tissue conditions, such as necrosis and small vessel disease, at a potential or determined amputation site.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Certain aspects relate to apparatuses and techniques for non-invasive and non-contact optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications including assessing the presence and severity of tissue conditions, such as necrosis and small vessel disease, at a potential or determined amputation site.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.