Diagnostic systems and methods for animal patients with global data center and local autonomous cell
The techniques described herein relate to systems and methods for diagnosing diseases in animal patients. In some cases, a method includes providing a set of global data to a global data center and processing the set of global data and categorizing it into data clusters, where each data cluster corresponds to a diagnostic indicator for assessment of a physiological or pathological condition. The method can further include measuring data of a local patient using measurement equipment of a local autonomous cell and communicating the local measurement data from the local autonomous cell to the global data center. The local measurement data can be processed, and the processed local measurement data can be compared with the data clusters to determine a local diagnostic indicator for the local patient. The local diagnostic indicator can then be communicated from the global data center to the local autonomous cell.
1 . A method for diagnosing diseases in animal patients comprising:
providing a set of global data to a global data center, including data from one or more tissue diffractometers;
training, by one or more processors, a machine learning algorithm, based on the set of global data, to generate a plurality of diagnostic indicators for assessment of physiological or pathological conditions;
categorizing, by the one or more processors and the trained machine learning algorithm, the set of global data into data clusters, where each data cluster is associated with a diagnostic indicator of the plurality of diagnostic indicators for a diagnosis of a disease;
measuring a local measurement data of a biological tissue of a patient using measurement equipment of a local autonomous cell, wherein the patient is an animal, the measurement equipment is a diffractometer configured to perform X-ray scattering measurements of a molecular structure of the biological tissue to collect diffraction pattern data related to ordering of the biological tissue, and the diffraction pattern data comprises a scattering pattern;
communicating the local measurement data and patient data from the local autonomous cell to the global data center;
pre-processing the local measurement data, by the one or more processors and the trained machine learning algorithm, to denoise, segment, mask, and/or enhance edges and/or features of the local measurement data;
processing the pre-processed local measurement data and comparing the processed local measurement data with the data clusters to determine one of the diagnostic indicators, wherein processing and comparing are performed by the one or more processors and the trained machine learning algorithm by operating upon the diffraction pattern data, and the diagnostic indicator includes a diagnosis that the patient has a disease; and
communicating the diagnostic indicator from the global data center to the local autonomous cell to be displayed on a user interface of a computer screen.
2 . The method of claim 1 , wherein the measuring the local measurement data comprises measuring a sample comprising α-keratin or collagen.
3 . The method of claim 1 , wherein the measuring the local measurement data comprises in vivo or in vitro measurement of a sample comprising hair, nail, skin, wool, horns, hooves, or tissue of an internal organ.
4 . The method of claim 1 , wherein the set of global data is provided from a certain geographical region or nation.
5 . The method of claim 1 , wherein the one or more processors is in a cloud, and uses cloud computing to process data using a distributed network of computers.
6 . The method of claim 1 , wherein the global data center comprises the one or more processors.
7 . The method of claim 1 , further comprising an analytical center in communication with the global data center, wherein the analytical center comprises the one or more processors.
8 . The method of claim 1 , further comprising:
measuring a plurality of local measurement data of a plurality of patients using a plurality of measurement equipment of a plurality of local autonomous cells, wherein:
the local measurement data is one of the plurality of local measurement data;
the patient is one of the plurality of patients;
the measurement equipment is one of the plurality of measurement equipment;
the local autonomous cell is one of the plurality of local autonomous cells; and
a plurality of patient data is associated with the plurality of patients;
communicating the plurality of local measurement data and the plurality of patient data from the plurality of local autonomous cells to the global data center;
processing the plurality of local measurement data using the one or more processors, wherein the processing is performed by the machine learning algorithm that determines a plurality of diagnostic indicators for the plurality of patients; and
communicating the plurality of diagnostic indicators from the global data center to the plurality of local autonomous cells.
9 . The method of claim 8 , wherein local autonomous cells of the plurality of local autonomous cells are in different geographic locations.
10 . The method of claim 1 , further comprising providing a biological tissue sample from the patient to the local autonomous cell to be measured using the measurement equipment.
11 . The method of claim 10 , wherein the biological tissue sample comprises one or more of a surgical sample, a resection sample, a pathology sample, or a biopsy sample.
12 . The method of claim 10 , wherein the biological tissue sample comprises one or more of hair, nail, skin, or tissue of an internal organ.
13 . The method of claim 10 , wherein the providing a biological tissue sample from the patient to the local autonomous cell comprises the patient or a veterinary services provider of the patient using a second user interface to input the patient data and sending the biological tissue sample by mail.
14 . The method of claim 1 , wherein the communicating the local measurement data and the patient data from the local autonomous cell to the global data center is done by an owner of the patient or a veterinary services provider of the patient using a second user interface.
15 . The method of claim 1 , further comprising communicating the diagnostic indicator from the local autonomous cell to an owner of the patient or a veterinary services provider of the patient using the user interface.
16 . The method of claim 1 , further comprising encrypting the local measurement data and the patient data using a data encryption device coupled to the measurement equipment, wherein the communicating the local measurement data and the patient data from the local autonomous cell to the global data center comprises communicating the encrypted local measurement data and encrypted the patient data from the local autonomous cell to the global data center.
17 . The method of claim 1 , wherein the global data center further comprises a global database stored on a central server or in a cloud, wherein the global database comprises the set of global data.
18 . The method of claim 1 , further comprising depersonalizing the local measurement data, the patient data, or any combination thereof before communicating the local measurement data and the patient data from the local autonomous cell to the global data center, wherein the communicating the local measurement data and the patient data from the local autonomous cell to the global data center comprises communicating the depersonalized local measurement data and the depersonalized patient data from the local autonomous cell to the global data center.
19 . The method of claim 18 , wherein a key for mapping the depersonalized local measurement data, the patient data, or any combination thereof is stored in a local institutional database or in an individual personal file of the patient.
20 . The method of claim 1 , wherein the patient data comprises descriptions or data related to symptoms of the patient comprising one or more of sleep disorders, insomnia, sudden awakenings, somnambulism, apnea, disorders of cerebral circulation, electroencephalogram (EEG) data, sudden convulsions and fainting, headaches and dizziness, and traumatic brain injuries.
21 . The method of claim 1 , wherein the diagnostic indicator relates to one of more of breast cancer, brain cancer, bone cancer, lung cancer, cervical cancer, bladder cancer, head cancer, neck cancer, kidney cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, throat cancer, oral cancer, vaginal cancer, sinus tachycardia of a heart, atrial extra-systoles, ventricular extra-systoles, acute myocardial infarction, heart valve defect, cardiomyopathy, ventricular hypertrophy, heart failure, arrhythmia, atrial fibrillation of the heart, pericarditis, bradycardia of the heart, myocarditis, ischemic stroke, neurological stroke, cognitive decline, glioblastoma, extra-cerebral tumors, bacterial endocarditis (inflammation of an inner lining of the heart), and meningococcal meningitis (inflammation of the membranes of a brain and spinal cord).
22 . The method of claim 1 , wherein the communicating the local measurement data and the patient data from the local autonomous cell to the global data center is done by mail, airmail, courier mail or e-mail.
23 . The method of claim 1 , wherein the communicating the local measurement data and the patient data from the local autonomous cell to the global data center or the communicating the diagnostic indicator from the global data center to the local autonomous cell occurs about once per day, or about once per week, or about once per month on average.