Abstract: Disclosed is an apparatus and method for imaging: a side-view of a object on a surface, a Contact Angle of a liquid object, the color of an object, or combinations thereof.

Abstract: Among other things, the present invention is related to devices and methods for improving optical analysis of a thin layer of a sample sandwiched between containing between two plates.

Abstract: The present disclosure relates to devices, apparatuses and methods of improving the accuracy of image-based assay, that uses imaging system having uncertainties or deviations (imperfection) compared with an ideal imaging system. One aspect of the present invention is to add the monitoring marks on the sample holder, with at least one of their geometric and/optical properties of the monitoring marks under predetermined and known, and taking images of the sample with the monitoring marks, and training a machine learning model using the images with the monitoring mark.

Abstract: Disclosed is an apparatus and method for imaging: a side-view of a object on a surface, a Contact Angle of a liquid object, the color of an object, or combinations thereof.

Abstract: A microscopy imaging system and methods for improving an assaying of a sample is disclosed.

Abstract: Among other things, the present invention is related to devices and methods for improving optical analysis of a thin layer of a sample sandwiched between containing between two plates.

Abstract: Among other things, the present invention is related to devices and methods for improving optical analysis of a thin layer of a sample sandwiched between two plates.

Abstract: A microscopy imaging system and methods for improving an assaying of a sample is disclosed.

Abstract: The present invention is related to, among other things, the devices and methods that improve the accuracy and reliability of an assay, even when the assay device and/or the operation of the assay device has certain errors, and in some embodiments the errors are random.

Abstract: The present disclosure relates to devices, apparatus and methods of improving the accuracy of image-based assay, that uses imaging system having uncertainties or deviations (imperfection) compared with an ideal imaging system. One aspect of the present invention is to add the monitoring marks on the sample holder, with at least one of their geometric and/optical properties of the monitoring marks under predetermined and known, and taking images of the sample with the monitoring marks, and train a machine learning model using the images with the monitoring mark.

Abstract: The disclosure provides an apparatus, a device, and methods for improving optical analysis of a thin layer of a sample between two plates, particularly for multiple wavelengths.

Abstract: Among other things, the present invention is related to devices and methods for improving optical analysis of a thin layer of a sample sandwiched between containing between two plates.

Abstract: The present invention is related to, among other things, the devices and methods that improve the accuracy and reliability of an assay, even when the assay device and/or the operation of the assay device has certain errors, and in some embodiments the errors are random.

Abstract: Among other things, the present invention is related to devices and methods for improving optical analysis of a thin layer of a sample sandwiched between containing between two plates.

Abstract: A method of assaying an analyte in a sample is disclosed. The method includes having a sample holder with a sample contact area for contacting a sample with an analyte, having a plurality of calibration structures on the sample contact area of the sample holder, imaging a part of the sample contact area that has the calibration structures, and using an algorithm that includes an image, calibration structures in the image, and artificial intelligence and/or machine learning to identify the analyte and/or determine the analyte concentration.

Abstract: The disclosure provides an apparatus, a device, and methods for improving optical analysis of a thin layer of a sample between two plates, particularly for multiple wavelengths.

Abstract: The present disclosure relates to devices, apparatus and methods of improving the accuracy of image-based assay, that uses imaging system having uncertainties or deviations (imperfection) compared with an ideal imaging system. One aspect of the present invention is to add the monitoring marks on the sample holder, with at least one of their geometric and/optical properties of the monitoring marks under predetermined and known, and taking images of the sample with the monitoring marks, and train a machine learning model using the images with the monitoring mark.

Abstract: The present invention is related to correct the errors in instruments, operation, and others using intelligent monitoring structures and machine learning, and others.

Abstract: A method of assaying an analyte in a sample is disclosed. The method includes having a sample holder with a sample contact area for contacting a sample with an analyte, having a plurality of calibration structures on the sample contact area of the sample holder, imaging a part of the sample contact area that has the calibration structures, and using an algorithm that includes an image, calibration structures in the image, and artificial intelligence and/or machine learning to identify the analyte and/or determine the analyte concentration.

Abstract: The disclosure provides a method for identifying a bio-entity including a cell type and count in a sample. The method includes: providing a device comprising a first plate, a second plate, and a patterned structural element; depositing the sample between the first and second plates; reducing the spacing of the first and second plates so that the first and second plates are in a closed configuration to compress the sample into a layer; and imaging the sample to obtain an image; and measuring and analyzing the image against a database generated with a machine learning model to obtain the bio-entity of the sample. The sample can be a blood sample, and the method can be a white blood cell differential test conducted with a mobile phone.