Diffraction-based global in vitro diagnostic system
An in vitro human-tissue analysis and communication system produces a quantitative diagnostic indicator for in vitro human-tissue analyzed by the system. The system includes a human-tissue-analyzer subsystem with at least one human-tissue analyzer that analyzes in vitro samples of human tissue and produces a quantitative-diagnostic indicator of each sample. The system also includes a two-way communication subsystem that allows the human-tissue-analyzer subsystem to send and receive information relevant to the quantitative-diagnostic indicators. The human-tissue-analyzer subsystem includes at least one tissue diffractometer operatively coupled to a computer database over a network, and configured for acquisition of the in vitro samples, and transfer of the corresponding human-tissue data to the computer database over the network. A computer processor is operatively coupled to the tissue diffractometer, and receives, transmits and processes the human-tissue data using a data analytics algorithm that provides a quantitative-diagnostic indicator of the in vitro sample of human tissue.
1. An in vitro human-tissue analysis and communication system that produces a quantitative diagnostic indicator for in vitro human-tissue analyzed by the system, comprising:
a human-tissue-analyzer subsystem that includes at least one human-tissue analyzer constructed to analyze in vitro samples of human tissue and to produce a quantitative-diagnostic indicator of each sample; and a two-way communication subsystem constructed to allow the human-tissue-analyzer subsystem to send and receive information relevant to the quantitative-diagnostic indicators, wherein the human-tissue-analyzer subsystem includes a plurality of tissue diffractometers located in plural and different geographic locations operatively coupled to a computer database over a network and the human-tissue-analyzer subsystem is configured for acquisition of in vitro samples of human-tissue data and to transfer of the human-tissue data to the computer database over the network,
at least one computer processor coupled to at least one tissue diffractometer of the plurality of tissue diffractometers is configured to receive the human-tissue data from the at least one tissue diffractometer and transmit the human-tissue data to the computer database; and
at least one additional computer processor coupled to the computer database configured to (i) process the human-tissue data using a data analytics algorithm that provides a quantitative diagnostic indicator of the in vitro sample of human tissue, (ii) receive data from a data group; (iii) transmit data from the data group; and (iv) process the data from the data group for a human subject using a data analytics algorithm that provides a quantitative diagnostic indicator for the in vitro sample or for a subject from which the in vitro sample was derived.
2. The system of claim 1 , further comprising a user interface that allows an individual subject or a healthcare provider to upload the individual subject's sample data for an in vitro sample to the computer database in exchange for processing of the sample data to receive the quantitative-diagnostic indicator for the in vitro sample or for the individual subject.
3. The system of claim 2 , wherein the user interface is further configured to allow an individual subject or their healthcare provider to make payments or upload an individual subject's signed consent form.
4. The system of claim 1 , wherein the at least one tissue diffractometer comprises a data encryption device that includes a global positioning system (GPS) positioning sensor and generates encrypted sample data, and wherein when transferred to the computer database the encrypted sample data is used to track changes in location of the at least one tissue diffractometer.
5. The system of claim 4 , wherein the at least one tissue diffractometer is configured to perform small angle X-ray scattering (SAXS) measurements.
6. The system of claim 5 , wherein the at least one tissue diffractometer is configured to perform wide angle X-ray scattering (WAXS) measurements.
7. The system of claim 6 , wherein the in vitro samples comprise a surgical sample, a resection sample, a pathology sample, a biopsy sample, or any combination thereof.
8. The system of claim 7 , wherein the sample data further comprises pathology lab image data, subject data, or any combination thereof.
9. The system of claim 1 , wherein the computer database resides on a central server.
10. The system of claim 9 , wherein the computer database resides in the cloud.
11. The system of claim 10 , wherein the sample data transferred to the computer database are depersonalized prior to the transfer.
12. The system of claim 11 , wherein a key for mapping the depersonalized sample data stored in the computer database to an individual subject is stored in a local institutional database or in the individual subject's personal files.
13. The system of claim 1 , wherein the data analytics algorithm comprises a statistical analysis of diffraction pattern data or a function thereof.
14. The system of claim 13 , wherein the statistical analysis comprises determination of a pair-wise distance distribution function, determination of a Patterson function, a calculation of a Porod invariant, a cluster analysis, a dispersion analysis, determination of one or more molecular structural periodicities, or any combination thereof.
15. The system of claim 14 , wherein the statistical analysis comprises a determination of a structural periodicity of collagen.
16. The system of claim 15 , wherein the statistical analysis comprises a determination of a structural periodicity of one or more lipids.
17. The system of claim 16 , wherein the statistical analysis comprises a determination of a structural periodicity of a tissue.
18. The system of claim 17 , wherein the data analytics algorithm comprises a machine learning algorithm.
19. The system of claim 18 , wherein the machine learning algorithm comprises a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a reinforcement learning algorithm, a deep learning algorithm, or any combination thereof.
20. The system of claim 19 , wherein the machine learning algorithm is a deep learning algorithm.
21. The system of claim 20 , wherein the deep learning algorithm is a convolutional neural network, a recurrent neural network, or a recurrent convolutional neural network.
22. The system of claim 21 , wherein the machine learning algorithm is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
23. The system of claim 22 , wherein the training dataset is updated as new sample data are uploaded to the computer database.
24. The system of claim 23 , wherein the sample data further comprises subject data comprising an individual subject's age, sex, ancestry data, genetic data, behavioral data, or any combination thereof, wherein the sample is from the individual subject.
25. The system of claim 24 , wherein the quantitative-diagnostic indicator for the in vitro sample comprises an indicator of a likelihood that the sample is positive or negative for a cancer.
26. The system of claim 25 , wherein the cancer comprises breast cancer, brain cancer, bone cancer, lung cancer, cervical cancer, bladder cancer, head and neck cancer, kidney cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, throat cancer, oral cancer, vaginal cancer, or any combination thereof.
27. The system of claim 26 , wherein the pathology lab image data comprises micrographs of stained in vitro tissue specimens.
28. The system of claim 25 , wherein the system is used to monitor the efficacy of a cancer therapeutic treatment.