Predicting neurodegenerative diseases based on speech analysis

Abstract

A method implemented by one or more computing devices includes receiving speech data associated with a patient and analyzing the speech data to quantify at least one speech variable over a period of time. The at least one speech variable includes a voiced pause variable. Thus, the method includes determining an estimate of tau accumulation based on the quantified at least one speech variable.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Application No. 63 / 319,650, filed March 14, 2022, the entire contents of which are incorporated herein by reference.

[0002] Technical Field The present application relates generally to speech analysis, and more particularly to techniques for predicting neurodegenerative diseases based on patient speech analysis. [Background technology]

[0003] background Identifying and detecting the signs of cognitive decline in patients can help more effectively prevent and treat neurodegenerative diseases such as Alzheimer's disease (AD) or other forms of dementia. Neurologically, AD may generally contain neurofibrillary tangles composed of modified tau protein, and thus, the accumulation of tau in the brain of an AD patient may be proportional to the progression of AD or the severity of AD experienced by the AD patient. Specifically, the accumulation of beta-amyloid in the brain of an AD patient may stop at the early clinical stage of cognitive decline, such as mild cognitive impairment (MCI) or mild neurocognitive disorder (NCD), but the accumulation of tau in the brain of an AD patient continues to increase with the progression of AD. In other words, the total amount of abnormal tau in the brain of an AD patient may indicate the stage, progression, and / or severity of AD.

[0004] Voice may generally include at least some indication of the patient's cognitive abilities. Furthermore, in patients with ubiquitous access to personal electronic devices that may be suitable for capturing the patient's voice, analysis of the voice sample for acoustic and linguistic characteristics and content may be readily performed. Thus, the patient's voice may provide an alternative to invasive procedures and / or intensive clinical and laboratory testing to screen patients for neurodegenerative diseases such as Alzheimer's disease (AD) or other forms of dementia. Summary of the Invention

[0005] overview The embodiments of the present disclosure relate to one or more computing devices, methods, and non-transitory computer-readable media that can be utilized to determine an estimate of a patient's tau accumulation based on quantified speech variables of at least voiced pauses and detect the severity and progression of a patient's neurodegenerative disease. Specifically, according to the embodiments of the present disclosure, the one or more computing devices can utilize one or more machine learning models (e.g., natural language processing (NLP) models, transformation-based language models, automatic speech recognition (ASR) models) to convert raw audio files of the patient's voice data into text transcripts and analyze one or more linguistic speech variables and / or one or more acoustic speech variables to determine an estimate of the patient's tau accumulation to which the patient's voice data corresponds. For example, in some embodiments, the one or more computing devices can analyze the text transcripts to calculate one or more of voiced pause speech variables, word frequency speech variables, lexical diversity speech variables, etc., which can then be correlated with one or more standardized uptake value ratio (SUVR) values ​​of one or more specific tau positron emission tomography (PET) tracers known to indicate the severity and progression of AD. Thus, the one or more processing devices may determine an estimate of tau accumulation based on one or more quantified voiced pause speech variables, word frequency speech variables, lexical diversity speech variables for a corresponding one of the plurality of patients, and thereby detect the severity and progression of AD in the patient.

[0006] In certain embodiments, the one or more computing devices may receive speech data associated with the patient. For example, in one embodiment, receiving the speech data may include receiving an audio file including an electronic record of the patient's speech. In certain embodiments, the one or more computing devices may analyze the speech data to quantify at least one speech variable, including a voiced pause variable, over a period of time. For example, in certain embodiments, analyzing the speech data to quantify the at least one speech variable may include analyzing the audio file to quantify at least one speech variable, including an acoustic speech variable. In certain embodiments, analyzing the audio file to quantify the voiced pause variable may include analyzing the audio file to identify inter-word pauses in the speech data, identifying pauses that qualify as voiced pauses, calculating a sum of a total number of inter-word pauses in the transcript and a total number of words in the transcript, dividing the number of voiced pauses by the sum, and returning the quantified voiced pause variable.

[0007] In certain embodiments, analyzing the speech data to quantify the at least one speech variable may further include generating a transcript based on the speech data and analyzing the transcript to quantify the at least one speech variable. In one embodiment, the at least one speech variable may include a linguistic speech variable. In certain embodiments, analyzing the speech data to quantify the voiced pause variable may include generating a transcript based on the speech data, analyzing the transcript to identify pauses between words in the transcript, identifying pauses that qualify as voiced pauses, calculating a sum of a total number of pauses between words in the transcript and a total number of words in the transcript, dividing the number of voiced pauses by the sum, and returning a quantified voiced pause variable. In certain embodiments, the linguistic speech variable may be determined by generating a transcript based on the speech data and analyzing the transcript to quantify the at least one speech variable. In one embodiment, the at least one speech variable may include a linguistic speech variable.

[0008] In certain embodiments, the at least one speech variable may include a word frequency variable. For example, in certain embodiments, the word frequency variable may be determined by calculating a frequency score for each of the words in the transcript, calculating an average frequency score for the transcript based on the frequency scores for each of the words in the transcript, and returning a quantified word frequency variable. In certain embodiments, the at least one speech variable may include a lexical diversity variable. For example, in certain embodiments, for each of a plurality of windows in the transcript, and each of the windows includes an approximately equal number of consecutive words in the transcript, the lexical diversity variable may be determined by calculating a number of unique words in the window, calculating a total number of words in the window, calculating a lexical diversity score for the window by dividing the number of unique words by the total number of words, calculating an average lexical diversity score for the transcript based on the lexical diversity score for each of the windows in the transcript, and returning a quantified lexical diversity variable.

[0009] In certain embodiments, analyzing the transcript may include utilizing one or more natural language processing (NLP) machine learning models to analyze the speech data. In certain embodiments, the one or more computing devices may then determine an estimate of tau accumulation based on at least one quantified speech variable. For example, in certain embodiments, determining an estimate of tau accumulation may include mapping one or more measures of positron emission tomography (PET) tracer uptake by tau present in the brain of the other subject to the quantified speech variable of the other subject, and estimating the amount of PET tracer uptake by tau predicted to be present in the brain of the patient based on the mapping and the one or more quantified speech variables of the patient. For example, in certain embodiments, the one or more measures of PET tracer uptake are [ 18 F]GTP1 tau PET tracer, F-18 flortau sipir tau PET tracer, (18)F-THK5351 tau PET tracer, [ 18The assay may include one or more measurements of uptake of a [F]MK-6240 tau PET tracer, a RO-948 tau PET tracer, a PI-2014 tau PET tracer, a PI-2620 tau PET tracer, or a T-808 tau PET tracer.

[0010] In certain embodiments, the one or more computing devices may classify the patient as having a neurodegenerative disease based on the estimate of tau accumulation. For example, in one embodiment, the neurodegenerative disease may include Alzheimer's disease (AD). In certain embodiments, the one or more computing devices may determine a progression rate of the neurodegenerative disease based on the identified change in at least one speech variable over a period of time. For example, in certain embodiments, the one or more computing devices may predict a change in the patient's performance on a clinical assessment selected from the group consisting of the Mini-Mental State Examination, the Clinical Dementia Rating Scale Questionnaire, the Clinical Dementia Rating-Sum of Boxes Questionnaire, the Alzheimer's Disease Rating Scale-Cognitive Subscale Battery, the Alzheimer's Disease Cooperative Study Group-Activities of Daily Living Questionnaire, the Neuropsychiatric Symptom Assessment, the Caregiver Global Impression of Alzheimer's Disease Scale, the Instrumental Activities of Daily Living Scale, and the Amsterdam Activities of Daily Living Questionnaire based on the progression rate of the neurodegenerative disease. In certain embodiments, the one or more computing devices may determine a progression rate of the neurodegenerative disease based on the patient's treatment regimen. In certain embodiments, the one or more computing devices may then send a notification regarding the estimate of tau accumulation to a computing device associated with the clinician. In certain embodiments, the one or more computing devices may also send a notification regarding the estimate of tau accumulation to an electronic device associated with the patient.

[0011] In certain embodiments, the one or more computing devices, in response to determining the estimate of tau accumulation, detect a signal that comprises at least one compound selected from the group consisting of compounds against oxidative stress, anti-apoptotic compounds, metal chelators, inhibitors of DNA repair, 3-amino-1-propanesulfonic acid (3APS), 1,3-propanedisulfonate (1,3PDS), secretase activators, beta- and gamma-secretase inhibitors, tau protein, anti-tau antibodies, anti-tau agents, gene therapy drugs, neurotransmitters, beta sheet disruptors, anti-inflammatory molecules, atypical antipsychotics, cholinesterase inhibitors, other drugs, and dietary supplements. A recommendation for administration of a therapeutic agent selected from the group consisting of therapeutic agents, symptomatic medications, neurological drugs, corticosteroids, antibiotics, antiviral agents, anti-tau antibodies, tau inhibitors, anti-amyloid beta antibodies, beta-amyloid aggregation inhibitors, target binding therapeutic agents, anti-BACE1 antibodies, BACE1 inhibitors, cholinesterase inhibitors, NMDA receptor antagonists, monoamine depleting agents, ergoloid mesylates, anticholinergic antiparkinsonian agents, dopaminergic antiparkinsonian agents, tetrabenazine, anti-inflammatory agents, hormones, vitamins, dimebolins, homotaurines, serotonin receptor activity modulators, interferons, and glucocorticoids may be generated. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 illustrates an exemplary embodiment of a telehealth service environment that may be utilized to determine an estimate of a patient's tau accumulation based on quantified speech variables of one or more voiced pauses and detect the severity and progression of a patient's neurodegenerative disease.

[0013] [Diagram 2] FIG. 13 is a tabular representation of cross-sectional correlations between total cortical grey [18F]GTP1 SUVR and global clinical scores.

[0014] [Diagram 3] FIG. 13 shows plots of cross-sectional correlations between total cortical gray [18F]GTP1 SUVR and speech variables.

[0015] [Figure 4A] FIG. 13 is a plot of the cross-sectional correlation between [18F]GTP1 SUVR and voiced pauses generalized across patient brain regions of interest (ROIs).

[0016] [Figure 4B] FIG. 13 is a plot of baseline voiced pauses correlated with increase in [18F]GTP1 SUVR over 18 months.

[0017] [Figure 5A] 1 is a flow diagram for determining an estimate of tau accumulation in a patient based on quantified speech variables of at least voiced pauses and detecting severity and progression of a neurodegenerative disease in the patient.

[0018] [Figure 5B] 1 is a flow diagram for determining an estimate of tau accumulation in a patient based on quantified speech variables of at least word frequency and detecting severity and progression of a neurodegenerative disease in the patient.

[0019] [Figure 5C] 1 is a flow diagram for determining an estimate of tau accumulation in a patient based on quantified speech variables of at least lexical diversity and detecting severity and progression of a neurodegenerative disease in the patient.

[0020] [Figure 6] FIG. 1 illustrates an exemplary computing system.

[0021] [Figure 7] FIG. 7 is a diagram of an exemplary artificial intelligence (AI) architecture included as part of the exemplary computing system of FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] Description of exemplary embodiments Identifying and detecting the signs of cognitive decline in patients can help more effectively prevent and treat neurodegenerative diseases such as Alzheimer's disease (AD) or other forms of dementia. Neurologically, AD may generally contain neurofibrillary tangles composed of modified tau protein, and thus, the accumulation of tau in the brain of an AD patient may be proportional to the progression of AD or the severity of AD experienced by the AD patient. Specifically, the accumulation of beta-amyloid in the brain of an AD patient may stop at the early clinical stage of cognitive decline, such as mild cognitive impairment (MCI) or mild neurocognitive disorder (NCD), but the accumulation of tau in the brain of an AD patient continues to increase with the progression of AD. In other words, the total amount of abnormal tau in the brain of an AD patient may indicate the stage, progression, and / or severity of AD.

[0023] Voice may generally include at least some indication of the patient's cognitive abilities. Furthermore, in patients with ubiquitous access to personal electronic devices that may be suitable for capturing the patient's voice, analysis of the voice sample for acoustic and linguistic characteristics and content may be readily performed. Thus, the patient's voice may provide an alternative to invasive procedures and / or intensive clinical and laboratory testing to screen patients for neurodegenerative diseases such as Alzheimer's disease (AD) or other forms of dementia.

[0024] Thus, the present disclosure relates to one or more computing devices, methods, and non-transitory computer-readable media that can be utilized to determine an estimate of a patient's tau accumulation based on quantified speech variables of at least voiced pauses and detect the severity and progression of a patient's neurodegenerative disease. Specifically, according to embodiments of the present disclosure, one or more computing devices can utilize one or more machine learning models (e.g., natural language processing (NLP) models, transformation-based language models, automatic speech recognition (ASR) models) to convert raw audio files of a patient's voice data into text transcripts and analyze one or more linguistic speech variables and / or one or more acoustic speech variables to determine an estimate of a patient's tau accumulation to which the patient's voice data corresponds. For example, in some embodiments, one or more computing devices can analyze the text transcripts to calculate one or more of voiced pause speech variables, word frequency speech variables, lexical diversity speech variables, etc., which can then be correlated with one or more SUVR values ​​of one or more specific tau PET tracers known to indicate the severity and progression of AD. Thus, the one or more processing devices may determine an estimate of tau accumulation based on one or more quantified voiced pause speech variables, word frequency speech variables, lexical diversity speech variables for a corresponding one of the plurality of patients, and thereby detect the severity and progression of AD in the patient.

[0025] As further described herein with respect to therapy or treatment:

[0026] Therapeutic agents may include neuronal-transmission enhancers, psychotherapeutic drugs, acetylcholinesterase inhibitors, calcium channel blockers, biogenic amines, benzodiazepine tranquilizers, acetylcholine synthesis, storage or release enhancers, acetylcholine postsynaptic receptor agonists, monoamine oxidase A or B inhibitors, N-methyl-D-aspartate glutamate receptor antagonists, nonsteroidal anti-inflammatory drugs, antioxidants, or serotonin receptor antagonists. In particular, therapeutic agents may include compounds against oxidative stress, anti-apoptotic compounds, metal chelators, inhibitors of DNA repair, such as pirenzepine and metabolites, 3-amino-1-propanesulfonic acid (3APS), 1,3-propanedisulfonic acid (1,3PDS), secretase activators, beta- and gamma-secretase inhibitors, tau protein, anti-tau antibodies or agents, neurotransmitters, beta-sheet breakers, anti-inflammatory molecules, "atypical antipsychotics", such as clozapine, ziprasin, cyclosporine ... The therapeutic agent may comprise at least one compound selected from the group consisting of doxorubicin, risperidone, aripiprazole or olanzapine, or cholinesterase inhibitors (ChEIs), such as tacrine, rivastigmine, donepezil, and / or galantamine, as well as other drugs or nutritional supplements, such as vitamin B12, cysteine, precursors of acetylcholine, lecithin, choline, ginkgo, acetyl-L-carnitine, idebenone, propentofylline, and / or xanthine derivatives.

[0027] In some embodiments, the therapeutic agent is a tau inhibitor.Non-limiting examples of tau inhibitors include methylthioninium, LMTX (also known as leuco-methylthioninium or Trx-0237, TauRx Therapeutics Ltd.), Rember™ (methylene blue or methylthioninium chloride [MTC], Trx-0014, TauRx Therapeutics Ltd), PBT2 (Prana Biotechnology), and PTI-51-CH3 (TauPro™, ProteoTech).

[0028] In some embodiments, the therapeutic agent is an anti-tau antibody. "Anti-tau immunoglobulin," "anti-tau antibody," and "antibody that binds to tau" are used interchangeably herein and refer to an antibody that can bind to tau (e.g., human tau) with sufficient affinity to be useful as a diagnostic and / or therapeutic agent in targeting tau. In some embodiments, the extent to which the anti-tau antibody binds to unrelated non-tau proteins is less than about 10% of the antibody's binding to tau, as measured, for example, by radioimmunoassay (RIA). In certain embodiments, the antibody that binds to tau has an affinity of 1 μM or less, 100 nM or less, 10 nM or less, 1 nM or less, 0.1 nM or less, 0.01 nM or less, or 0.001 nM or less (e.g., 10 -8 M or less, e.g. 10 -8 M~10 -13 M, for example 10 -9 M~10 -13 Dissociation constant (K D ). In certain embodiments, the anti-tau antibody binds to an epitope of tau that is conserved among tau from different species. In some cases, the antibody binds to monomeric tau, oligomeric tau, and / or phosphorylated tau. In some embodiments, the anti-tau antibody binds to monomeric tau, oligomeric tau, non-phosphorylated tau, and phosphorylated tau with comparable affinities, such as affinities that differ from each other by 50-fold or less. In some embodiments, an antibody that binds to monomeric tau, oligomeric tau, non-phosphorylated tau, and phosphorylated tau is referred to as a "pan-tau antibody." In some embodiments, the anti-tau antibody binds to an epitope within the N-terminal region of tau, e.g., an epitope within / spanning residues 2-24, e.g., residues 6-23. In a specific embodiment, the anti-tau antibody is semolinemab.

[0029] In some embodiments, the anti-tau antibody is one or more selected from the group consisting of different N-terminal binders, mid-domain binders, and fibrillar tau binders. Non-limiting examples of other anti-tau antibodies include BIIB092 or BMS-986168 (Biogen, Bristol-Myers Squibb), APN-mAb005 (Aprinoia Therapeutics / Samsung Biologics), BIIB076 (Biogen / Eisai), ABBV-8E12 or C2N-8E12 (AbbVie, C2N Diagnostics, LLC), WO2012049570, WO2014028777, WO2014165271, WO2014100600, WO2015200806, antibodies disclosed in U.S. Pat. No. 8,980270 or U.S. Pat. No. 8,980271, E2814 (Eisai), goslanemab (Biogen), tirabonemab (Abbvie), and zagopenemab (Lilly).

[0030] In some embodiments, the therapeutic agent is an anti-tau agent. Non-limiting examples include BIIB080 (Biogen / Ionis), LY3372689 (Lilly), PNT001 (Pinteon Therapeutics), OLX-07010 (Oligomerix, Inc.), TRx-0237 / LMTX (TauRx), JNJ-63733657 (Janssen), tau siRNA (Lilly / Dicerna), and PSY-02 (Psy Therapeutics).

[0031] These include GV-971 (Green Valley), CT1812 (Cognition Therapeutics), and ATH-1017 (Athira). Pharma), COR388 (Cortexyme), Carbohydrates (Cassava), Drugs (Novo Nordisk), Cosmetics (Anavex Life). Sciences) AR1001 (AriBio) BE (KeifeRx / Life Molecular Imaging / Sun Pharma, ALZ-801 (Alzheon), AL003 (Alector / AbbVie), Lomecel-B (Longeveron), UB-311 (Vaxxinity), XPro1595 / Manufacturer (INmune Bio), NLY-01 (D&D). Biotech) Protein / PQ912(Vivoryon / Nordic / Simcere) Protein (Novartis) Protein (New Amsterdam). Pharma)、AADvac1(Axon Neuroscience)、ANVS-401 / Posiphen(Annovis Bio)、TB006(TureBinding)、BI 474121(Boehringer). Ingelheim, NuCerin (Shaperon / Kukjeon), ALZ-101 (Alzinova), NNI-362 (Neuronascent), MK-1942 (Merck), E2511 (Eisai), IGC-AD1 (India Globalization Capital), AL001 (Alzamend). Neuro) AL002 (Alzamend Neuro) AL101 (Alector / GSK) MW-151 (ImmunoChem Therapeutics, DNL-788 / SAR443820 (Denali / Sanofi), ALN-APP (Alnylam / Regeneron), E2F4DN (Tetraneuron), EmtinB (NeuroScientific Biopharma), NIT-001 (Neurostech), ACD679 (AlzeCure). Pharma)、ACD680(AlzeCure Pharma)、YDC-103(YD Global Life).and at least one compound for treating AD selected from the group consisting of: Bioscience), BMD-001 (Biorchestra), STL-101 (Stellate Therapeutics), AV-1959R (Nuravax), AV1959D (Nuravax), AV1980R (Nuravax), Duvax (Nuravax), dapanstril (Olatec Therapeutics), LX1001 (Cornell University), BDNF (UC San Diego), ST-501 (Biogen), AMT-240 (uniQure), SOL-410 (Sola), SOL-258 (Sola), AAVhmAb, SHP-231 (Shape), SHP-232 (Shape), TEL-01 (Telocyte), GT-0007X (Gene Therapy).

[0032] In some embodiments, the therapeutic agent is a general misfolding inhibitor, such as NPT088 (NeuroPhage Pharmaceuticals).

[0033] In some embodiments, the therapeutic agent is a neurological drug. Neurological drugs include, but are not limited to, beta secretase, presenilin, amyloid precursor protein or a portion thereof, amyloid beta peptide or an oligomer or fibril thereof, death receptor 6 (DR6), receptor for advanced glycation end products (RAGE), parkin, and huntingtin; NMDA receptor antagonists (i.e., memantine), monoamine depleting agents (i.e., tetrabenazine); ergoloid mesylates; anticholinergic parkinsonism agents (i.e., procyclidine, diphenhydramine, trihexylphenidyl, benztropine, biperiden, and trihexyphenidyl); dopaminergic parkinsonism agents (i.e., entacapone, selegiline, pramipexole, bromocriptine, rotigotine, selegiline, ropinirole, rasagiline, apomorphine, carbidopa, levothyroxine ... vodopa, pergolide, tolcapone and amantadine; tetrabenazine; anti-inflammatory agents (including but not limited to nonsteroidal anti-inflammatory drugs (i.e., indomethacin and other compounds listed above)); hormones (i.e., estrogen, progesterone and leuprolide); vitamins (i.e., folic acid and nicotinamide); dimebolins; homotaurines (i.e., 3-aminopropanesulfonic acid, 3APS); serotonin receptor activity modulators (i.e., xaliproden); interferons, and antibodies or other binding molecules (including but not limited to small molecules, peptides, aptamers, or other protein binding agents) that specifically bind to a target selected from glucocorticoids or corticosteroids. The term "corticosteroid" includes, but is not limited to, fluticasone (including fluticasone propionate (FP)), beclomethasone, budesonide, ciclesonide, mometasone, flunisolide, betamethasone, and triamcinolone. "Inhalable corticosteroid" means a corticosteroid suitable for delivery by inhalation. Exemplary inhalable corticosteroids are fluticasone, beclomethasone propionate, budesonide, mometasone furoate, ciclesonide, flunisolide, and triamcinolone acetonide.

[0034] In certain certain embodiments, the therapeutic agent is one or more selected from the group consisting of corticosteroids, antibiotics, antiviral agents, anti-tau antibodies, tau inhibitors, anti-amyloid beta antibodies, beta-amyloid aggregation inhibitors, anti-BACE1 antibodies, BACE1 inhibitors; therapeutic agents that specifically bind to a target; cholinesterase inhibitors; NMDA receptor antagonists; monoamine depleting agents; ergoloid mesylates; anticholinergic parkinsonism agents; dopaminergic parkinsonism agents; tetrabenazine; anti-inflammatory agents; hormones; vitamins; dimebolins; homotaurines; serotonin receptor activity modulators; interferons, and glucocorticoids.

[0035] Non-limiting examples of anti-Abeta antibodies include crenezumab, solanezumab (Lilly), bapineuzumab, aducanumab, gantenerumab, donanemab (Lilly), LY3372993 (Lilly), ACU193 (Acumen Pharmaceuticals), SHR-1707 (Hengrui USA / Atridia), ALZ-201 (Alzinova), PMN-310 (ProMIS neurosciences), and lecanemab (BAN-2401, Biogen, Eisai Co., Ltd.). Non-limiting exemplary beta-amyloid aggregation inhibitors include ELND-005 (also called AZD-103 or scyllo-inositol), tramiprosate, and PTI-80 (Exebryl-1®, ProteoTech). Non-limiting examples of BACE inhibitors include E-2609 (Biogen, Eisai Co., Ltd.), AZD3293 (also known as LY3314814, AstraZeneca, Eli Lilly & Co.), MK-8931 (verubecestat), and JNJ-54861911 (Janssen, Shionogi Pharma).

[0036] In some embodiments, the therapeutic agent is an "atypical antipsychotic" such as, for example, clozapine, ziprasidone, risperidone, aripiprazole, or olanzapine for the treatment of positive and negative psychotic symptoms including hallucinations, delusions, thought disorder (manifested by prominently disorganized, deviant, and / or nonlinear thinking), and bizarre or disorganized behavior, as well as anhedonia, affective flattening, affect blunting, and social withdrawal.

[0037] In some embodiments, other therapeutic agents include, for example, those described in WO 2004 / 058258 (see in particular pages 16 and 17), including therapeutic agent targets (pages 36-39), alkanesulfonic acids and alkanol sulfates (pages 39-51), cholinesterase inhibitors (pages 51-56), NMDA receptor antagonists (pages 56-58), estrogens (pages 58-59), nonsteroidal anti-inflammatory drugs (pages 60-61), antioxidants (pages 61-62), peroxisome proliferator-activated receptors (PGRs), and the like. (PPAR) agonists (pages 63-67), cholesterol lowering agents (pages 68-75); amyloid inhibitors (pages 75-77), amyloid formation inhibitors (pages 77-78), metal chelators (pages 78-79), antipsychotics and antidepressants (pages 80-82), nutritional supplements (pages 83-89) and compounds which increase the availability of biologically active substances in the brain (see pages 89-93) as well as prodrugs (pages 93 and 94), this document being incorporated herein by reference, in particular the compounds mentioned on the pages indicated above.

[0038] As further described herein with respect to indications of tau accumulation:

[0039] The terms "tau pathology", "tauopathy", "tau protein-associated disease" or "tau-associated disease" are used interchangeably herein and refer to a group of diseases and disorders caused by or associated with tau aggregates in the extracellular space of the brain of a patient, including those caused by or associated with the formation of neurofibrillary or neuropil threads. Such diseases include, but are not limited to, neurological disorders such as AD, and diseases or conditions characterized by loss of cognitive ability. Non-limiting examples of tau pathologies include amyotrophic lateral sclerosis, Parkinson's disease, Creutzfeldt-Jakob disease, punch-drunk syndrome, Down's syndrome, Gerstmann-Sträussler-Scheinker disease, inclusion body myositis, prion protein cerebral amyloid angiopathy, traumatic brain injury, Guam amyotrophic lateral sclerosis / parkinsonism dementia syndrome, non-Guam motor neuron disease with neurofibrillary tangles, argyrophilic grain dementia, corticobasal degeneration, diffuse neurofibrillary tangles with calcifications, frontotemporal dementia, frontotemporal dementia linked to chromosome 17 with parkinsonism, Hallervorden-Spatz disease, multiple system atrophy, Niemann-Pick disease type C, pallido-ponto-nigral degeneration, and pallidal-ponto-nigral degeneration. degeneration), Pick's disease, progressive subcortical gliosis, progressive supranuclear palsy, subacute sclerosing panencephalitis, neurofibrillary tangle dementia, postencephalitic parkinsonism, and myotonic dystrophy. In some embodiments, the tauopathy is progressive supranuclear palsy.

[0040] As further described herein with respect to measuring the severity and progression of Alzheimer's disease:

[0041] The Mini-Mental State Examination ("MMSE") is a brief clinical cognitive test commonly used to screen for dementia and other cognitive deficits (Folstein et al. J Psychiatr Res 1975;12:189-98). The MMSE provides a total score of 0-30. A score of 26 or less is generally considered to indicate a deficit. The lower the numerical score on the MMSE, the greater the deficit or impairment of the tested patient compared to another individual with a higher score. An increase in the MMSE score may indicate an improvement in the patient's condition, while a decrease in the MMSE score may signify a worsening of the patient's condition. In some embodiments, a stable MMSE score may indicate a slowing, delay or halt in the progression of AD, or a lack of emergence of new clinical, functional or cognitive symptoms or deficits, or an overall stabilization of the disease.

[0042] The Clinical Dementia Rating Scale ("CDR") (Morris Neurology 1993;43:2412-4) is a semi-structured interview resulting in five degrees of impairment of performance in each of six categories of cognitive-based function: memory, orientation, judgment and problem solving, social problems, household and hobbies, and personal care. The CDR was originally designed with a global score: 0-no dementia, 0.5-probable dementia, 1-mild dementia, 2-moderate dementia, 3-severe dementia.

[0043] The complete CDR-SB score is based on the sum of the scores across all six boxes. Subscores can also be obtained for each box or component separately, for example, CDR / memory or CDR / judgment and problem solving. As used herein, a "deterioration in CDR-SB performance" or an "increase in CDR-SB score" indicates a worsening of the patient's condition and can reflect the progression of AD.

[0044] The term "CDR-SB" refers to the Clinical Dementia Rating-Sum of Boxes, which provides a score of 0 to 18 (O'Bryant et al., 2008, Arch Neurol 65:1091-1095). The CDR-SB score is based on semi-structured interviews of patient and caregiver informants, resulting in five degrees of impairment of performance for each of six categories of cognitively based functioning: memory, orientation, judgment / problem solving, community issues, home and hobbies, and personal care. The test is administered to both the patient and the caregiver, and each component (or each "box") is scored on a scale of 0 to 3 (the five degrees are 0, 0.5, 1, 2, and 3). The sum of the scores of the six categories is the CDR-SB score. A decrease in the CDR-SB score may indicate an improvement in the patient's symptoms, whereas an increase in the CDR-SB score may indicate a worsening of the patient's symptoms. In some embodiments, a stable CDR-SB score may indicate a slowing, delay or halt in the progression of AD, or a lack of emergence of new clinical, functional or cognitive symptoms or impairments, or an overall stabilization of the disease.

[0045] The Alzheimer's Disease Assessment Scale-Cognitive Subscale ("ADAS Cog") is a frequently used measure to assess cognition in clinical trials for mild to moderate AD (Rozzini et al. Int J Geriatr Psychiatry 2007;22:1217-22.; Connor and Sabbagh, J Alzheimers Dis. 2008;15:461-4; Ihl et al. Int J Geriatr Psychiatry 2012;27:15-21). The ADAS-Cog is an examiner-administered battery that assesses multiple cognitive domains, including memory, comprehension, praxis, orientation, and spontaneous speech (Rosen et al. 1984, Am J Psychiatr 141:1356-64; Mohs et al. 1997, Alzheimer Dis Assoc Disord 11(S2):S13-S21). ADAS-Cog is the standard primary endpoint in AD treatment trials (Mani 2004, Stat Med 23:305-14). The higher the numerical score of ADAS-Cog, the greater the deficit or impairment of the tested patient compared to another individual with a lower score. ADAS-Cog can be used to evaluate whether a treatment for AD is therapeutically effective. An increase in ADAS-Cog score indicates a worsening of the patient's condition, and a decrease in ADAS-Cog score indicates an improvement of the patient's condition. In some embodiments, a stable ADAS-Cog score can indicate a slowing, delay, or halt in the progression of AD, or a lack of emergence of new clinical or cognitive symptoms or disorders, or an overall stabilization of the disease.

[0046] The ADAS-Cog12 is a 70-point version of the ADAS-Cog plus a 10-point delayed word recall item that assesses recall of a list of learned words. The ADAS-Cog11 is another version with a range of 0 to 70. Other ADAS-Cog scales include the ADAS-Cogl3 and ADAS-Cogl4.

[0047] A decrease in the ADAS-Cog11 score may indicate an improvement in the patient's condition, whereas an increase in the ADAS-Cog11 score may indicate a worsening of the patient's condition. In some embodiments, a stable ADAS-Cog11 score may indicate a slowing, delay, or halt in the progression of AD, or a reduction in the progression of clinical or cognitive decline, or the absence of the appearance of new clinical or cognitive symptoms or disorders, or an overall stabilization of the disease.

[0048] The component subtests of the ADAS-Cog11 can be grouped into three cognitive domains: memory, language, and praxis (Verma et al. Alzheimer's Research & Therapy 2015). This "breakdown" can improve sensitivity in measuring cognitive decline, for example, when focusing on mild to moderate AD stages (Verma, 2015). Thus, the ADAS-Cog11 score can be analyzed for changes in each of the three cognitive domains: memory domain, language domain, and praxis domain. The memory domain value of the ADAS-Cog11 score may be referred to herein as the "ADAS-Cog11 memory domain score" or simply the "memory domain." A slowdown in memory decline may refer to a reduction in the rate of decline in memory capacity and / or function, retention of memory, and / or a reduction in memory loss. A slowdown in memory decline can be evidenced, for example, by a smaller (or less negative) score in the ADAS-Cog11 memory domain.

[0049] Similarly, the language domain value of the ADAS-Cog11 score may be referred to herein as the "ADAS-Cog11 language domain score" or simply the "language domain score," and the praxis domain value of the ADAS-Cog11 score may be referred to herein as the "ADAS-Cog11 praxis domain score" or simply the "praxis domain." Praxis can refer to the planning and / or execution of simple tasks, and / or praxis can refer to the ability to conceptualize, plan, and execute complex sequences of motor movements, as well as copy drawings or three-dimensional structures, and following commands.

[0050] The memory domain score is further divided into components, including scores reflecting the subject's ability to recognize and / or recall words, thereby assessing the ability of "word recognition" or "word recall". The word recognition assessment of the ADAS-Cog11 memory domain score may be referred to herein as the "ADAS-Cog11 word recognition score" or simply the "word recognition score". For example, equivalent alternative forms of subtests for word recall and word recognition may be used in successive test applications of a given patient. A slowdown in memory decline may be evidenced, for example, by a smaller (or less negative) score on the word recognition component of the ADAS-Cog11 memory domain.

[0051] The Alzheimer's Disease Cooperative Study Group-Activities of Daily Living Inventory or Alzheimer's Disease Cooperative Study Group-Activities of Daily Living Scale ("ADCS-ADL;" Galasko et al. Alzheimer Dis Assoc Disord 1997;11(Suppl2):S33-9) is the most widely used scale for assessing functional outcome in AD patients (Vellas et al. Lancet Neurol. 2008;7:436-50). Scores range from 0 to 78, with higher scores indicating better ADL function. The ADCS-ADL is applied to caregivers and covers both basic ADLs (e.g., eating and toileting) and more complex or instrumental ADLs (e.g., using the telephone, managing finances, preparing meals) (Galasko et al. Alzheimer Disease and Associated Disorders, 1997 11(Suppl2),S33-S39).

[0052] The Neuropsychiatric Symptom Index ("NPI") (Cummings et al. Neurology 1994;44:2308-14) is a widely used scale that evaluates the behavioral symptoms of AD, including their frequency, severity, and associated distress. Individual symptom scores range from 0 to 12, and total NPI scores range from 0 to 144. The NPI is administered to caregivers and refers to the patient's behavior over the past month.

[0053] The Alzheimer's Disease Caregiver Global Impression Scale ("CaGI-Alz") is a novel scale used in the clinical trials described herein that consists of four items to assess caregiver perceptions of changes in the patient's disease severity. All items are rated on a 7-point Likert-type scale ranging from 1 (much improved since treatment initiation / previous CaGI Alz assessment) to 7 (much worse since treatment initiation / previous CaGI Alz assessment).

[0054] The term "iADL" refers to the Instrumental Activities of Daily Living Scale (Lawton, MP, and Brody, EM, 1969, Gerontologist 9:179-186). This scale measures the ability to perform typical daily activities such as housework, laundry, operating the telephone, shopping, preparing meals, etc. The lower the score, the more impaired the individual is in performing activities of daily living.

[0055] Another measure that may be used is the Amsterdam Activities of Daily Living Questionnaire (A-IADL-Q).

[0056] 1 illustrates an exemplary embodiment of a telehealth service environment 100 that may be utilized to determine an estimate of a patient's tau accumulation based on quantified speech variables of at least voiced pauses and detect the severity and progression of a patient's neurodegenerative disease, according to an embodiment of the present disclosure. As illustrated, the telehealth service environment 100 may include a plurality of patients 102A, 102B, 102C, and 102D, each associated with a respective electronic device 104A, 104B, 104C, and 104D, which may be suitable for enabling the plurality of patients 102A, 102B, 102C, and 102D to launch and collaborate with a respective telehealth application 106A (e.g., "Telehealth App1"), 106B (e.g., "Telehealth App2"), 106C (e.g., "Telehealth App3"), and 106D (e.g., "Telehealth AppN"). 1, each of the electronic devices 104A, 104B, 104C, and 104D may be connected to a telehealth services platform 112 via one or more communication network(s) 110. In particular embodiments, the telehealth services platform 112 may include a cloud-based computing architecture suitable for hosting and servicing the telehealth applications 106A (e.g., “Telehealth App1”), 106B (e.g., “Telehealth App2”), 106C (e.g., “Telehealth App3”), and 106D (e.g., “Telehealth AppN”) executing on each of the electronic devices 104A, 104B, 104C, and 104D.For example, in one embodiment, the telemedicine services platform 112 may include a Platform as a Service (PaaS) architecture, a Software as a Service (SaaS) architecture, an Infrastructure as a Service (IaaS) architecture, a Compute as a Service (CaaS) architecture, a Data as a Service (DaaS) architecture, a Database as a Service (DBaaS) architecture, or other similar cloud-based computing architecture (e.g., "X" as a Service (XaaS)).

[0057] In certain embodiments, as further illustrated by FIG. 1, the telemedicine services platform 112 may include one or more processing devices 114 (eg, servers) and one or more data stores 116. For example, in some embodiments, the one or more processing devices 114 (e.g., a server) may include one or more general purpose processors, graphics processing units (GPUs), application specific integrated circuits (ASICs), systems on chips (SoCs), microcontrollers, field programmable gate arrays (FPGAs), central processing units (CPUs), application processors (APs), visual processing units (VPUs), neural processing units (NPUs), neural decision processors (NDPs), deep learning processors (DLPs), tensor processing units (TPUs), neuromorphic processing units (NPUs), or any of a variety of other processing device(s) or accelerators that may be suitable for providing processing and / or computing support for the telehealth applications 106A (e.g., “Telehealth App1”), 106B (e.g., “Telehealth App2”), 106C (e.g., “Telehealth App3”), and 106D (e.g., “Telehealth AppN”). Similarly, the data store 116 may include one or more internal databases that may be utilized to store, for example, information associated with multiple patients 102A, 102B, 102C, and 102D (e.g., audio files of patient voice data 118).

[0058] In particular embodiments, as described above, the telehealth service platform 112 may be a hosting service platform for telehealth applications 106A (e.g., “Telehealth App1”), 106B (e.g., “Telehealth App2”), 106C (e.g., “Telehealth App3”), and 106D (e.g., “Telehealth AppN”) running on respective electronic devices 104A, 104B, 104C, and 104D. For example, in some embodiments, telehealth applications 106A (e.g., “Telehealth App1”), 106B (e.g., “Telehealth App2”), 106C (e.g., “Telehealth App3”), and 106D (e.g., “Telehealth AppN”) may each include a telehealth mobile application (e.g., a mobile application) that may be utilized to, for example, enable multiple patients 102A, 102B, 102C, and 102D to remotely access health care and medical care services and / or to collaborate with one or more patient-selected clinicians (e.g., clinician 126) as part of on-demand health care services.

[0059] In certain embodiments, one or more of the plurality of patients 102A, 102B, 102C, and 102D may include one or more patients with AD, one or more patients suspected of having AD, and / or one or more patients susceptible to developing AD. Thus, as further illustrated by FIG. 1, in certain embodiments, one or more of the plurality of patients 102A, 102B, 102C, and 102D may undergo a speech-based assessment that is utilized to determine an estimate of tau accumulation in one or more of the plurality of patients 102A, 102B, 102C, and 102D utilizing quantified speech variables of at least voiced pauses. For example, in certain embodiments, one or more of the multiple patients 102A, 102B, 102C, and 102D may input audio 108A, 108B, 108C, and 108D utilizing telehealth applications 106A (e.g., “Telehealth App1”), 106B (e.g., “Telehealth App2”), 106C (e.g., “Telehealth App3”), and 106D (e.g., “Telehealth AppN”) executing on respective electronic devices 104A, 104B, 104C, and 104D. For example, in some embodiments, the input audio 108A, 108B, 108C, and 108D may include, for example, electronic recordings of the speech of the multiple patients 102A, 102B, 102C, and 102D. In particular embodiments, the input audio 108A, 108B, 108C, 108D may be performed in response to one or more requests provided by the telehealth service platform 112 to one or more of the multiple patients 102A, 102B, 102C, and 102D, for example, via telehealth applications 106A (e.g., “Telehealth App1”), 106B (e.g., “Telehealth App2”), 106C (e.g., “Telehealth App3”), and 106D (e.g., “Telehealth AppN”).In other embodiments, one or more of the multiple patients 102A, 102B, 102C, and 102D may record input audio 108A, 108B, 108C, 108D using one or more microphones of their respective electronic devices 104A, 104B, 104C, and 104D without first being instructed via the telehealth application 106A (e.g., “Telehealth App1”), 106B (e.g., “Telehealth App2”), 106C (e.g., “Telehealth App3”), and 106D (e.g., “Telehealth AppN”).

[0060] For example, in some embodiments, as part of the audio-based assessment, the telemedicine service platform 112 may generate and provide one or more audio-based tasks to one or more of the patients 102A, 102B, 102C, and 102D that instruct them to make audio and recordings via one or more microphones of the respective electronic devices 104A, 104B, 104C, and 104D. In one embodiment, the audio-based assessment may include, for example, a description of an image that may be displayed via the telemedicine application 106A, 106B, 106C, and 106D, a reading of a passage of a book that may be presented via the telemedicine application 106A, 106B, 106C, and 106D, a series of question-answering tasks that may be presented via the telemedicine application 106A, 106B, 106C, and 106D, or other audio-based assessments with medical-grade neuropsychological speech and language assessments. In certain embodiments, the audio-based assessments may be performed at different times over some given period of time. For example, in some embodiments, an audio-based assessment may be performed on an initial date, then on one or more dates selected from the group including, for example, approximately 0.25 months, 0.5 months, 0.75 months, 1 month, 3 months, 6 months, 9 months, 12 months, 15 months, 18 months, 21 months, 24 months, 27 months, 30 months, 33 months, and / or 36 months from the initial date.

[0061] In certain embodiments, once one or more of the plurality of patients 102A, 102B, 102C, and 102D complete the audio-based assessment, one or more of the respective electronic devices 104A, 104B, 104C, and 104D may transmit one or more audio files of the patient voice data 118 to the telemedicine services platform 112. In certain embodiments, the one or more audio files of the patient voice data 118 may be stored in one or more data stores 116 of the telemedicine services platform 112. In certain embodiments, the one or more processing devices 114 (e.g., a server) may then access the one or more audio files of the patient voice data 118 and analyze the one or more audio files of the patient voice data 118 to quantify one or more voice variables utilizing the one or more audio files of the patient voice data 118. For example, in certain embodiments, the one or more processing devices 114 (e.g., a server) may utilize one or more machine learning models (e.g., natural language processing (NLP) models, transformation-based language models, automatic speech recognition (ASR) models) to convert raw audio files of the patient's speech data 118 into text representations (e.g., transcripts) or other representational data, e.g., to determine one or more linguistic speech variables and / or one or more acoustic speech variables. For example, in some embodiments, the one or more linguistic speech variables and / or one or more acoustic speech variables may include voiced pause speech variables, word frequency speech variables, lexical diversity speech variables, etc.

[0062] For example, in certain embodiments, the one or more processing devices 114 (e.g., a server) may analyze one or more audio files of the patient's voice data 118 to quantify voiced pause speech variables by analyzing one or more audio files of the patient's voice data 118 to identify pauses between words in the one or more audio files of the patient's voice data 118. In certain embodiments, the one or more processing devices 114 (e.g., a server) may identify pauses between words by first identifying pauses that qualify as voiced pauses. For example, in some embodiments, the one or more processing devices 114 (e.g., a server) may identify pauses that qualify as voiced pauses by, for example, measuring excessive use of speech fillers between spoken words (e.g., "Um," "Ah," "Er," "Uh," "You know," "Like," "I mean," "Kinda," "Hmm," "Kinda like"). In another embodiment, one or more processing devices 114 (e.g., a server) may identify pauses that qualify as voiced pauses, for example, by measuring extensive silence between spoken words (e.g., periods of silence greater than 2 seconds, greater than 3 seconds, greater than 4 seconds, or greater than 5 seconds).

[0063] In certain embodiments, the one or more processing devices 114 (e.g., a server) may then calculate the sum of the total number of pauses between words in the transcript of the audio file of the patient's voice data 118 and the total number of words in the transcript of the audio file of the patient's voice data 118, and divide the number of voiced pauses by the sum. In certain embodiments, the one or more processing devices 114 (e.g., a server) may analyze one or more audio files of the patient's voice data 118 to quantify the word frequency phonetic variable. For example, in certain embodiments, the one or more processing devices 114 (e.g., a server) may identify the word frequency phonetic variable by calculating a frequency score for each of the words in the transcript of the audio file of the patient's voice data 118 and utilizing the calculated frequency scores to calculate an average frequency score for the transcript of the audio file of the patient's voice data 118.

[0064] In certain embodiments, the one or more processing devices 114 (e.g., a server) may analyze one or more audio files of the patient's voice data 118 to quantify lexical diversity speech variables. For example, in certain embodiments, for each of a plurality of windows in the transcript of the audio file of the patient's voice data 118, the one or more processing devices 114 (e.g., a server) may calculate a lexical diversity score for the window by calculating the number of unique words in the window, calculating the total number of words in the window, and dividing the number of unique words by the total number of words. In one embodiment, each window may include, for example, the same number of consecutive words in the transcript of the audio file of the patient's voice data 118. In certain embodiments, the one or more processing devices 114 (e.g., a server) may then utilize the lexical diversity scores calculated for each of the windows in the transcript of the audio file of the patient's voice data 118 to calculate an average lexical diversity score for the transcript of the audio file of the patient's voice data 118.

[0065] In certain embodiments, upon quantifying the voiced pause speech variables, word frequency speech variables, lexical diversity speech variables, etc., the one or more processing devices 114 (e.g., a server) may then correlate one or more of the quantified voiced pause speech variables, word frequency speech variables, and lexical diversity speech variables with a standardized uptake value ratio (SUVR) of one or more specific tau positron emission tomography (PET) tracers known to be indicative of AD severity and progression to generate an estimate 120 of tau accumulation for one of the multiple patients 102A, 102B, 102C, and 102D to which the audio file transcript of the patient's voice data 118 corresponds.

[0066] For example, as further described in connection with Figures 3, 4A, and 4B, one or more processing devices 114 (e.g., a server) may perform, for example, one or more Pearson correlations between: 1) one or more measures of uptake of a tau PET tracer representative of tau present in the brain of another patient (e.g., SUVR) to the quantified speech variables of the other patient; and 2) one or more of the quantified voiced pause speech variables, word frequency speech variables, and lexical diversity speech variables. For example, in certain embodiments, the one or more measures of uptake of a tau PET tracer are [ 18 F]GTP1 tau PET tracer, F-18 flortau sipir tau PET tracer, (18)F-THK5351 tau PET tracer, [ 18 F]MK-6240 tau PET tracer, RO-948 tau PET tracer, PI-2014 tau PET tracer, PI-2620 tau PET tracer, or T-808 tau PET tracer uptake (e.g., SUVR).

[0067] In certain embodiments, utilizing the mapping and one or more of the quantified voiced pause speech variables, word frequency speech variables, and lexical diversity speech variables for a corresponding one of the plurality of patients 102A, 102B, 102C, and 102D, the one or more processing devices 114 (e.g., a server) may estimate the amount of PET tracer uptake (e.g., SUVR) by tau predicted to be present in the brain of the corresponding one of the plurality of patients 102A, 102B, 102C, and 102D. In particular, in some embodiments, one or more particular tau PET tracers may provide biomarkers indicative of AD severity and progression. Thus, according to embodiments of the present disclosure, by correlating one or more of the quantified voiced pause speech variables, word frequency speech variables, lexical diversity speech variables for a corresponding one of the plurality of patients 102A, 102B, 102C, and 102D to SUVR values ​​of one or more particular tau PET tracers known to be indicative of AD severity and progression, one or more processing devices 114 (e.g., a server) may determine an estimate of tau accumulation based on one or more of the quantified voiced pause speech variables, word frequency speech variables, lexical diversity speech variables for a corresponding one of the plurality of patients 102A, 102B, 102C, and 102D, thereby detecting the severity and progression of AD for the corresponding one of the plurality of patients 102A, 102B, 102C, and 102D.

[0068] Indeed, although the present embodiments may be discussed primarily with respect to detecting the severity and progression of AD tauopathy, the present embodiments, which determine estimates of tau accumulation based on one or more of quantified voiced pause speech variables, word frequency speech variables, and lexical diversity speech variables, may also be used to detect and assess the severity and progression of AD tauopathy, e.g., Pick's disease tauopathy, Progressive Supranuclear Palsy (PSP) tauopathy, Corticobasal Degeneration (CBD) tauopathy, Argyrophilic Grain Disease (AGD) tauopathy, Globular Glial Tauopathy, and other disorders. It should be understood that the present invention may be utilized to detect the severity and progression of various tauopathies (e.g., neurodegenerative diseases or disorders characterized by deposition of abnormal tau protein in the brain), including primary age-related tauopathy (PART), neurofibrillary tangle predominant dementia (NFTPD) tauopathy, chronic traumatic encephalopathy (CTE) tauopathy, Parkinson's disease (PD) tauopathy, or age-related tau astrogliosis (ARTAG) tauopathy, etc.

[0069] In certain embodiments, as part of generating the estimate 120 of tau accumulation (and / or the estimate 120 of AD progression), the one or more processing devices 114 (e.g., a server) may determine a rate of progression of AD by measuring change in one or more of the quantified voiced pause speech variables, word frequency speech variables, lexical diversity speech variables for a corresponding one of the multiple patients 102A, 102B, 102C, and 102D over a period of time. For example, in some embodiments, utilizing the determined rate of AD progression, the one or more processing devices 114 (e.g., a server) may predict a change in performance of a corresponding one of the plurality of patients 102A, 102B, 102C, and 102D on a clinical assessment selected from a battery of clinical assessments including, for example, the Mini-Mental State Examination (MMSE), the Clinical Dementia Rating (CDR) scale questionnaire, the Clinical Dementia Rating-Sum of Boxes (CDR-SB) questionnaire, the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) battery of tests, the Alzheimer's Disease Cooperative Study Group-Activities of Daily Living questionnaire, the Neuropsychiatric Symptom Assessment, the Caregiver Global Impression of Alzheimer's Disease Scale, the Instrumental Activities of Daily Living scale, and the Amsterdam Activities of Daily Living Questionnaire.

[0070] 1, the one or more processing devices 114 (e.g., a server) may then transmit the generated estimates of tau accumulation 120 (and / or estimates of AD progression 120) to a computing device 122 and present a notification or report 124 to a clinician 126, which may be associated with a corresponding one of the plurality of patients 102A, 102B, 102C, and 102D. In an embodiment, the one or more processing devices 114 (e.g., a server) may also transmit the generated estimates of tau accumulation 120 (and / or estimates of AD progression 120) to a corresponding one of the plurality of patients 102A, 102B, 102C, and 102D via the respective electronic devices 104A, 104B, 104C, or 104D. In particular embodiments, the clinician 126 may review the notification or report 124 and communicate with a corresponding one of the multiple patients 102A, 102B, 102C, and 102D via a respective telehealth application 106A (e.g., “Telehealth App1”), 106B (e.g., “Telehealth App2”), 106C (e.g., “Telehealth App3”), or 106D (e.g., “Telehealth AppN”) regarding the cognitive health of the corresponding one of the multiple patients 102A, 102B, 102C, and 102D.

[0071] For example, in certain embodiments, based on the medical review and analysis of the generated tau accumulation estimates 120 (and / or AD progression estimates 120), the clinician 126 may communicate via the computing device 122 a recommendation for administration of a treatment or therapeutic regimen for a corresponding one of the plurality of patients 102A, 102B, 102C, and 102D. In response to receiving input from the clinician 126 via the computing device 122, the one or more processing devices 114 (e.g., a server) may process at least one of the following compounds selected from a set including, for example, compounds against oxidative stress, anti-apoptotic compounds, metal chelators, inhibitors of DNA repair, 3-amino-1-propanesulfonic acid (3APS), 1,3-propanedisulfonate (1,3PDS), secretase activators, beta- and gamma-secretase inhibitors, tau protein, anti-tau antibodies, anti-tau agents, gene therapy agents, neurotransmitters, beta sheet breakers, anti-inflammatory molecules, atypical antipsychotics, cholinesterase inhibitors, other drugs, and dietary supplements. In one embodiment, the method may generate a recommendation for administration of a therapeutic agent selected from the set including a therapeutic agent consisting of at least one compound, a symptomatic agent, a neurological agent, a corticosteroid, an antibiotic, an antiviral agent, an anti-tau antibody, a tau inhibitor, an anti-amyloid beta antibody, a beta-amyloid aggregation inhibitor, a therapeutic agent binding to a target, an anti-BACE1 antibody, a BACE1 inhibitor, a cholinesterase inhibitor, an NMDA receptor antagonist, a monoamine depleting agent, an ergoloid mesylate, an anticholinergic antiparkinsonian agent, a dopaminergic antiparkinsonian agent, tetrabenazine, an anti-inflammatory agent, a hormone, a vitamin, a dimebolin, a homotaurine, a serotonin receptor activity modulator, an interferon, and a glucocorticoid. In certain embodiments, the one or more processing devices 114 (e.g., a server) may then transmit, via the respective electronic device 104A, 104B, 104C, or 104D, a notification regarding a recommendation for administration of a treatment or therapeutic regimen for a corresponding one of the plurality of patients 102A, 102B, 102C, and 102D, respectively.

[0072] FIG. 2 shows a total cortical gray [ 18A table 200 of cross-sectional correlations between [F]GTP1 SUVR and global clinical scores is shown. As shown, the table 200 shows Pearson's correlations between Tau-Worst-Case Gain Sensitivity (Tau-WCGS) cognitive scores 201, which are correlated with CDR-SB global cognitive scores 202 (e.g., "0.19"), ADAS-Cog global cognitive scores 204 (e.g., "0.41"), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) global cognitive scores 206 (e.g., "-0.44"), Mini-Mental State Examination (MMSE) global cognitive scores 208 (e.g., "-0.23"), and Alzheimer's Disease Cooperative Study-Activities of Daily Living Scale (ADCS-ADL) global cognitive scores 210 (e.g., "-0.07"), respectively. Specifically, in certain embodiments, the global cognitive scores 202, 204, 206, 208, and 210 may represent, for example, a Pearson's correlation coefficient (e.g., one or more numbers from -1 to +1 that reflect the tendency for two random variables to have a linear association), and may generally indicate that tau accumulation as represented by the tau WCGS cognitive score 201 correlates with AD severity and progression.

[0073] FIG. 3 shows a total cortical gray [ 18 3 shows a plot 300 of cross-sectional correlations between [F]GTP1 SUVR and speech variables. In one embodiment, the cross-sectional correlations of plot 300 were generated based on patient speech data and PET scan data collected from one or more patients. As shown, in accordance with an embodiment of the present disclosure, plot 300 shows cross-sectional correlations between tau PET tracer [F]GTP1 SUVR and speech variables. 18 F] Plot of Pearson correlation between SUVR of GTP1 and quantified voiced pause speech variables. 302 Tau PET tracer [ 18 F] Plots of Pearson correlations between SUVR of GTP1 and quantified word frequency speech variables, and tau PET tracer [ 183 shows a plot 306 of Pearson's correlation between SUVR of F]GTP1 and quantified lexical diversity speech variables. Specifically, in certain embodiments, plots 302, 304, and 306 show Pearson's correlation coefficients (e.g., "R" represents the number of -1 to +1 that reflects the tendency of two random variables to have a linear association) for the quantified voiced pause speech variable (e.g., R=0.46), the quantified word frequency speech variable (e.g., R=0.3), and the quantified lexical diversity speech variable (e.g., R=-0.28), respectively. Thus, plots 302, 304, and 306 show that tau accumulation correlates with the quantified voiced pause speech variable, the quantified word frequency speech variable, and the quantified lexical diversity speech variable, respectively, and that the quantified voiced pause speech variable has the strongest correlation with tau accumulation compared to the quantified word frequency speech variable and the quantified lexical diversity speech variable.

[0074] FIG. 4A illustrates a plot 400A of a cross-sectional correlation between [18F]GTP1 SUVR and voiced pauses generalized across a patient brain region of interest (ROI), in accordance with an embodiment of the present disclosure. In one embodiment, the cross-sectional correlation of plot 400A was generated based on patient speech data and PET scan data collected from one or more patients. As shown, plot 400A illustrates ... 18 F] Plot of Pearson correlation between SUVR of GTP1 and quantified voiced pause speech variables for the left superior temporal gyrus of the patient brain. 402, Tau PET tracer [ 18 F] Plot of Pearson correlation between SUVR of GTP1 and quantified voiced pause speech variables for the left inferior frontal gyrus of the patient's brain, and tau PET tracer [ 184 shows a plot 406 of Pearson's correlation between SUVR of [F]GTP1 and quantified voiced pause speech variables for the left inferior parietal lobule of the patient's brain. Specifically, in certain embodiments, plots 402, 404, and 406 respectively show Pearson's correlation coefficients for quantified voiced pause speech variables for the left superior temporal gyrus of the patient's brain (e.g., R=0.47), quantified voiced pause speech variables for the left inferior frontal gyrus of the patient's brain (e.g., R=0.35), and quantified voiced pause speech variables for the left inferior parietal lobule of the patient's brain (e.g., R=0.47), where the quantified voiced pause speech variables for the left superior temporal gyrus of the patient's brain and the left inferior parietal lobule of the patient's brain are correlated with the tau PET tracer [F]GTP1 SUVR and quantified voiced pause speech variables for the left inferior parietal lobule of the patient's brain (e.g., R=0.47). 18 F]GTP1 SUVR has the strongest correlation.

[0075] FIG. 4B shows a graph of the 18-month 18 4 shows a plot 400B of baseline pause speech correlating with increases in [F]GTP1 SUVR. In one embodiment, the correlation of plot 400B was generated based on patient speech data and PET scan data collected from one or more patients over an 18-month period. As shown, plot 400B shows a correlation between baseline voiced pauses and [F]GTP1 SUVR over an 18-month period for the total cortical gray of the patient's brain. 18 F] Plot of Pearson correlation between increase in GTP1 SUVR and baseline voiced pauses over 18 months for the left inferior frontal gyrus of the patient's brain. 18 Figure 410 Plot of Pearson's correlation between increase in [F]GTP1 SUVR and baseline voiced pauses over 18 months for the left inferior parietal lobule of the patient's brain. 18[F]GTP1 SUVR increase. Specifically, in certain embodiments, plots 408, 410, and 412 respectively show Pearson correlation coefficients for quantified voiced pause speech variables for the left superior temporal gyrus (e.g., R=0.37), for the left inferior frontal gyrus (e.g., R=0.34), and for the left inferior parietal lobule (e.g., R=0.33) of the patient's brain, with baseline voiced pauses for total cortical gray matter of the patient's brain having the strongest correlation with tau accumulation over 18 months.

[0076] FIG. 5A shows a flow diagram 500A for determining an estimate of tau accumulation in a patient based on quantified speech variables of at least voiced pauses and detecting the severity and progression of a neurodegenerative disease in the patient, according to an embodiment of the present disclosure. Flowchart 500A may be implemented utilizing one or more processing devices (e.g., computing systems and artificial intelligence architectures described below in connection with FIGS. 6 and 7) that may include hardware (e.g., a general purpose processor, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a system on a chip (SoC), a microcontroller, a field programmable gate array (FPGA), a central processing unit (CPU), an application processor (AP), a visual processing unit (VPU), a neural processing unit (NPU), a neural decision processor (NDP), a deep learning processor (DLP), a tensor processing unit (TPU), a neuromorphic processing unit (NPU), or any other processing device(s) that may be suitable for processing various medical profile data and making one or more decisions based thereon), software (e.g., instructions operating / executing on one or more processors), firmware (e.g., microcode), or some combination thereof.

[0077] Flowchart 500A may begin at block 502 with one or more processing devices receiving speech data associated with a patient. Flowchart 500A may then proceed to block 504 where the one or more processing devices analyze the speech data to quantify at least one speech variable, including a voiced pause variable, over a period of time. Flowchart 500A may then end at block 506 where the one or more processing devices determine an estimate of tau accumulation based on the quantified at least one speech variable.

[0078] FIG. 5B shows a flow diagram 500B for determining an estimate of a patient's tau accumulation based on quantified speech variables of at least word frequency and detecting the severity and progression of a neurodegenerative disease in a patient, according to an embodiment of the present disclosure. Flowchart 500B may be implemented utilizing one or more processing devices (e.g., computing systems and artificial intelligence architectures described below in connection with FIGS. 6 and 7) that may include hardware (e.g., a general purpose processor, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a system on a chip (SoC), a microcontroller, a field programmable gate array (FPGA), a central processing unit (CPU), an application processor (AP), a visual processing unit (VPU), a neural processing unit (NPU), a neural decision processor (NDP), a deep learning processor (DLP), a tensor processing unit (TPU), a neuromorphic processing unit (NPU), or any other processing device(s) that may be suitable for processing various medical profile data and making one or more decisions based thereon), software (e.g., instructions operating / executing on one or more processors), firmware (e.g., microcode), or some combination thereof.

[0079] Flowchart 500B may begin with one or more processing devices receiving speech data associated with a patient at block 508. Flowchart 500B may then proceed to block 510 where the one or more processing devices analyze the speech data to quantify at least one speech variable, including a word frequency variable, over a period of time. Flowchart 500B may then end at block 512 where the one or more processing devices determine an estimate of tau accumulation based on the quantified at least one speech variable.

[0080] FIG. 5C shows a flow diagram 500C for determining an estimate of a patient's tau accumulation based on quantified speech variables of at least lexical diversity and detecting the severity and progression of a neurodegenerative disease in a patient, according to an embodiment of the present disclosure. Flowchart 500C may be implemented utilizing one or more processing devices (e.g., computing systems and artificial intelligence architectures described below in connection with FIGS. 6 and 7) that may include hardware (e.g., a general purpose processor, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a system on a chip (SoC), a microcontroller, a field programmable gate array (FPGA), a central processing unit (CPU), an application processor (AP), a visual processing unit (VPU), a neural processing unit (NPU), a neural decision processor (NDP), a deep learning processor (DLP), a tensor processing unit (TPU), a neuromorphic processing unit (NPU), or any other processing device(s) that may be suitable for processing various medical profile data and making one or more decisions based thereon), software (e.g., instructions operating / executing on one or more processors), firmware (e.g., microcode), or some combination thereof.

[0081] Flowchart 500C may begin with one or more processing devices receiving speech data associated with a patient at block 514. Flowchart 500C may then proceed to block 516 where the one or more processing devices analyze the speech data to quantify at least one speech variable, including a lexical diversity variable, over a period of time. Flowchart 500C may then end at block 518 where the one or more processing devices determine an estimate of tau accumulation based on the quantified at least one speech variable.

[0082] Thus, as generally illustrated by flow chart 500A of FIG. 5A, flow chart 500B of FIG. 5B, and flow chart 500C of FIG. 5C, the present embodiments relate to one or more computing devices, methods, and non-transitory computer-readable media that may be utilized to determine an estimate of a patient's tau accumulation based on quantified speech variables of at least voiced pauses and detect the severity and progression of a neurodegenerative disease in a patient. Specifically, according to embodiments of the present disclosure, one or more computing devices may utilize one or more machine learning models (e.g., natural language processing (NLP) models, transformation-based language models, automatic speech recognition (ASR) models) to convert raw audio files of the patient's voice data into text transcripts and analyze one or more linguistic speech variables and / or one or more acoustic speech variables to determine an estimate of the patient's tau accumulation to which the patient's voice data corresponds. For example, in some embodiments, one or more computing devices may analyze the text transcript to calculate one or more of a voiced pause speech variable, a word frequency speech variable, a lexical diversity speech variable, etc., which may then be correlated with one or more SUVR values ​​of one or more specific tau PET tracers known to be indicative of AD severity and progression. Thus, the one or more processing devices may determine an estimate of tau accumulation based on one or more quantified voiced pause speech variables, word frequency speech variables, lexical diversity speech variables for a corresponding one of the plurality of patients, thereby detecting the severity and progression of AD in the patient.

[0083] FIG. 6 illustrates an exemplary computing system 600 that may be utilized to determine an estimate of a patient's tau accumulation based on quantified speech variables of at least voiced pauses and detect the severity and progression of a patient's neurodegenerative disease, according to an embodiment of the present disclosure. In certain embodiments, the computing system 600 may perform one or more steps of one or more methods described or illustrated herein. In certain embodiments, the computing system 600 provides functionality described or illustrated herein. In certain embodiments, software operating on the computing system 600 performs one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein. Certain embodiments include one or more portions of the computing system 600. As used herein, reference to a computer system may encompass a computing device, and vice versa, where appropriate. Moreover, reference to a computer system may encompass one or more computer systems, where appropriate.

[0084] The present disclosure contemplates any suitable number of computing systems 600. The present disclosure contemplates computing system 600 taking any suitable physical form. By way of example and not limitation, computing system 600 may be an embedded computer system, a system on a chip (SOC), a single board computer system (SBC) (e.g., a computer on module (COM) or system on module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile phone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented / virtual reality device, or a combination of two or more of these. Where appropriate, computing system 600 may include one or more computing systems 600, may be single or distributed, may span multiple locations, span multiple machines, span multiple data centers, or may reside in a cloud that may include one or more cloud components in one or more networks.

[0085] Where appropriate, computing system 600 may perform one or more steps of one or more methods described or illustrated herein without substantial spatial or temporal limitations. By way of example and not by way of limitation, computing system 600 may perform one or more steps of one or more methods described or illustrated herein in real time or in batch mode. Computing system 600 may perform one or more steps of one or more methods described or illustrated herein at different times or in different locations, where appropriate.

[0086] In a particular embodiment, computing system 600 includes a processor 602, memory 604, storage 606, input / output (I / O) interface 608, communication interface 610, and bus 612. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement. In a particular embodiment, processor 602 includes hardware for executing instructions, such as instructions that make up a computer program. By way of example and not by way of limitation, to execute instructions, processor 602 may retrieve (or fetch) instructions from an internal register, an internal cache, memory 604, or storage 606, decode and execute those instructions, and then write one or more results to an internal register, an internal cache, memory 604, or storage 606. In a particular embodiment, processor 602 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 602 including any suitable number of any suitable internal caches, where appropriate. By way of example, and not by way of limitation, processor 602 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in an instruction cache may be copies of instructions in memory 604 or storage 606, and the instruction cache may speed up fetching of those instructions by processor 602.

[0087] The data in the data cache may be a copy of data in memory 604 or storage 606 on which instructions executing in processor 602 operate, results of previous instructions executed in processor 602 for access by subsequent instructions executing in processor 602 or for writing to memory 604 or storage 606, or other suitable data. The data cache may speed up read or write operations by processor 602. The TLB may speed up virtual-address translation for processor 602. In particular embodiments, processor 602 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 602 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 602 may include one or more arithmetic logic units (ALUs), be a multi-core processor, or include one or more processors 602. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

[0088] In a particular embodiment, memory 604 includes a main memory for storing instructions for processor 602 to execute or data on which processor 602 operates. By way of example and not by way of limitation, computing system 600 may load instructions into memory 604 from storage 606 or another source (such as, for example, another computing system 600). Processor 602 may then load the instructions from memory 604 into an internal register or cache. To execute instructions, processor 602 may retrieve instructions from the internal register or cache and decode those instructions. During or after execution of instructions, processor 602 may write one or more results (which may be intermediate or final results) to an internal register or cache. Processor 602 may then write one or more of those results to memory 604.

[0089] In particular embodiments, the processor 602 executes only instructions in one or more internal registers or internal caches or in memory 604 (as opposed to storage 606 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 604 (as opposed to storage 606 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may connect the processor 602 to the memory 604. The bus 612 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between the processor 602 and the memory 604 and facilitate accesses to the memory 604 requested by the processor 602. In particular embodiments, the memory 604 includes random access memory (RAM). This RAM may be volatile memory, where appropriate. This RAM may be dynamic RAM (DRAM) or static RAM (SRAM), where appropriate. Moreover, this RAM may be single-ported RAM or multi-ported RAM, where appropriate. This disclosure contemplates any suitable RAM. Memory 604 may include one or more memories 604, where appropriate. Although this disclosure describes and illustrates a particular memory, this disclosure contemplates any suitable memory.

[0090] In particular embodiments, storage 606 includes mass storage for data or instructions. By way of example and not limitation, storage 606 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Storage 606 may include removable or non-removable (or fixed) media, where appropriate. Storage 606 may be internal or external to computing system 600, where appropriate. In particular embodiments, storage 606 is a non-volatile solid-state memory. In particular embodiments, storage 606 includes read-only memory (ROM). Where appropriate, this ROM may be a mask program ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. The present disclosure contemplates mass storage 606 taking any suitable physical form. Storage 606 may include one or more storage control units to facilitate communications between processor 602 and storage 606, as appropriate. Storage 606 may include one or more storages 606, as appropriate. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.

[0091] In particular embodiments, I / O interface 608 includes hardware, software, or both that provide one or more interfaces for communication between computing system 600 and one or more I / O devices. Computing system 600 may include one or more of these I / O devices, as appropriate. One or more of these I / O devices may enable communication between a person and computing system 600. By way of example and not by way of limitation, the I / O devices may include a keyboard, a keypad, a microphone, a monitor, a mouse, a printer, a scanner, a speaker, a still camera, a stylus, a tablet, a touch screen, a trackball, a video camera, another suitable I / O device, or a combination of two or more of these. The I / O devices may include one or more sensors. This disclosure contemplates any suitable I / O devices and any suitable I / O interfaces 606 for those I / O devices. As appropriate, I / O interface 608 may include one or more device or software drivers that enable processor 602 to drive one or more of these I / O devices. I / O interface 608 may include one or more I / O interfaces 606, where appropriate. Although this disclosure describes and illustrates a particular I / O interface, this disclosure contemplates any suitable I / O interface.

[0092] In particular embodiments, communication interface 610 includes hardware, software, or both that provide one or more interfaces for communication (e.g., packet-based communication, etc.) between computing system 600 and one or more other computer systems 600 or one or more networks. By way of example and not by way of limitation, communication interface 610 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network, or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface 610 for that network.

[0093] By way of example, and not by way of limitation, computing system 600 may communicate with one or more portions of an ad-hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or the Internet, or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. By way of example, computing system 600 may communicate with a wireless PAN (WPAN) (e.g., a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (e.g., a Global System for Mobile Communications (GSM) network), or other suitable wireless networks, or a combination of two or more of these. Computing system 600 may include any suitable communication interface 610 for any of these networks, where appropriate. Communication interface 610 may include one or more communication interfaces 610, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.

[0094] In particular embodiments, bus 612 includes hardware, software, or both that connects components of computing system 600 to one another. By way of example, and not by way of limitation, bus 612 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI Express (PCIe) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus, or a combination of two or more of these. Bus 612 may include one or more buses 612, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

[0095] As used herein, one or more computer-readable non-transitory storage media may include, where appropriate, one or more semiconductor-based or other integrated circuits (ICs) (such as, for example, field programmable gate arrays (FPGAs) or application specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical disks, optical disk drives (ODDs), magneto-optical disks, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid state drives (SSDs), RAM drives, secure digital cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.

[0096] FIG. 7 shows a diagram 700 of an exemplary artificial intelligence (AI) architecture 702 (which may be included as part of the computing system 600, as described above with respect to FIG. 6) that may be utilized to determine an estimate of a patient's tau accumulation based on quantified speech variables of at least voiced pauses and detect the severity and progression of a neurodegenerative disease in a patient, according to an embodiment of the present disclosure. In particular embodiments, the AI ​​architecture 702 may be implemented utilizing one or more processing devices that may include, for example, hardware (e.g., a general purpose processor, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a system on a chip (SoC), a microcontroller, a field programmable gate array (FPGA), a central processing unit (CPU), an application processor (AP), a visual processing unit (VPU), a neural processing unit (NPU), a neural decision processor (NDP), a deep learning processor (DLP), a tensor processing unit (TPU), a neuromorphic processing unit (NPU), and / or any other processing device(s) that may be suitable for processing various medical profile data and making one or more decisions based thereon), software (e.g., instructions operating / executing on one or more processing devices), firmware (e.g., microcode), or some combination thereof.

[0097] In certain embodiments, as illustrated in FIG. 7, the AI ​​architecture 702 may include machine learning (ML) algorithms and functions 704, natural language processing (NLP) algorithms and functions 706, expert systems 708, computer-based vision algorithms and functions 710, speech recognition algorithms and functions 712, planning algorithms and functions 714, and robotics algorithms and functions 716. In certain embodiments, the ML algorithms and functions 704 may include any statistically-based algorithms that may be suitable for finding patterns across large amounts of data (e.g., “big data” such as genomics data, proteomics data, metabolomics data, metagenomics data, transcriptomics data, medication data, medical diagnostic data, medical procedure data, medical diagnosis data, medical symptom data, demographic data, patient lifestyle data, physical activity data, family history data, socioeconomic data, geographic environment data, etc.). For example, in certain embodiments, the ML algorithms and functions 704 may include deep learning algorithms 718, supervised learning algorithms 720, and unsupervised learning algorithms 722.

[0098] In certain embodiments, the deep learning algorithms 718 may include any artificial neural network (ANN) that can be utilized to learn deep levels of representations and abstractions from large amounts of data. For example, the deep learning algorithms 718 may include ANNs such as perceptrons, multi-layer perceptrons (MLPs), autoencoders (AEs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memories (LSTMs), graded recurrent units (GRUs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), and deep Q-networks, neural autoregressive distribution estimation (NADEs), adversarial networks (ANs), attention models (AMs), spiking neural networks (SNNs), deep reinforcement learning, and the like.

[0099] In certain embodiments, the supervised learning algorithm 720 may include any algorithm that may be utilized to apply what has been learned in the past to new data, for example, using labeled examples to predict future events. For example, starting from an analysis of a known training data set, the supervised learning algorithm 720 may create an inferred function to make a prediction regarding the output value. The supervised learning algorithm 600 may also compare its output to the correct intended output and find errors in order to correct the supervised learning algorithm 720 accordingly. On the other hand, the unsupervised learning algorithm 722 may include any algorithm that may be applied, for example, when the data used to train the unsupervised learning algorithm 722 is not classified or labeled. For example, the unsupervised learning algorithm 722 may study and analyze how a system may infer functions to describe hidden structures from unlabeled data.

[0100] In particular embodiments, the NLP algorithms and functions 706 may include any algorithms or functions that may be suitable for automatically manipulating natural language, such as speech and / or text. For example, in some embodiments, the NLP algorithms and functions 706 may include a content extraction algorithm or function 724, a classification algorithm or function 726, a machine translation algorithm or function 728, a question answering (QA) algorithm or function 730, and a text generation algorithm or function 732. In particular embodiments, the content extraction algorithm or function 724 may include a means for extracting text or images from electronic documents (e.g., web pages, text editor documents, etc.) for use in other applications, for example.

[0101] In certain embodiments, classification algorithm or function 726 may include any algorithm that may utilize a supervised learning model (e.g., logistic regression, naive Bayes, stochastic gradient descent (SGD), k-nearest neighbors, decision trees, random forests, support vector machines (SVM), etc.) to learn from and make new observations or classifications based on data input into the supervised learning model. Machine translation algorithm or function 728 may include any algorithm or function that may be suitable for automatically converting source text in one language, for example, into text in another language. QA algorithm or function 730 may include any algorithm or function that may be suitable for automatically answering questions posed by a human in natural language, such as those implemented by a voice-controlled personal assistant device. Text generation algorithm or function 732 may include any algorithm or function that may be suitable for automatically generating natural language text.

[0102] In certain embodiments, the expert system 708 may include any algorithm or function that may be suitable for simulating the judgment and actions of a human or organization with expertise and experience in a particular field (e.g., stock trading, medicine, sports statistics, etc.). The computer-based vision algorithms and functions 710 may include any algorithm or function that may be suitable for automatically extracting information from images (e.g., photographic images, video images). For example, the computer-based vision algorithms and functions 710 may include image recognition algorithms 734 and machine vision algorithms 736. The image recognition algorithms 734 may include any algorithm that may be suitable for automatically identifying and / or classifying objects, places, people, etc. that may be included in one or more image frames or other display data. The machine vision algorithms 736 may include any algorithm that may be suitable for enabling a computer to "see" or that may be suitable for example to rely on an image sensor camera with specialized optics to acquire images to process, analyze, and / or measure various data characteristics for decision-making purposes.

[0103] In certain embodiments, speech recognition algorithms and functions 712 may include any algorithms or functions that may be suitable for recognizing and translating spoken language into text, such as through automatic speech recognition (ASR), computer speech recognition, speech-to-text (STT) 738, or text-to-speech (TTS) 740, for computing to communicate with one or more users via voice. In certain embodiments, planning algorithms and functions 714 may include any algorithms or functions that may be suitable for generating a sequence of actions, each action may include its own set of preconditions to be satisfied before performing the action. Examples of AI planning may include classical planning, reduction to other problems, temporal planning, probabilistic planning, preference-based planning, conditional planning, etc. Finally, robotics algorithms and functions 716 may include any algorithms, functions, or systems that may enable one or more devices to replicate human behavior, such as through movements, gestures, performance tasks, decision-making, emotions, etc.

[0104] As used herein, "or" is inclusive and not exclusive, unless expressly indicated otherwise or indicated otherwise by context. Thus, as used herein, "A or B" means "A, B, or both," unless expressly indicated otherwise or indicated otherwise by context. Moreover, "and" is both jointly and severally, unless expressly indicated otherwise or indicated otherwise by context. Thus, as used herein, "A and B" means "A and B, jointly or severally," unless expressly indicated otherwise or indicated otherwise by context.

[0105] As used herein, "automatically" and its derivatives mean "without human intervention" unless expressly indicated otherwise or indicated otherwise by context.

[0106] The embodiments disclosed herein are merely illustrative and the scope of the disclosure is not limited thereto. The embodiments according to the present disclosure are disclosed in the appended claims, particularly for methods, storage media, systems, and computer program products, and any feature recited in one claim category, e.g., method, may also be claimed in another claim category, e.g., system. Dependencies or references in the appended claims are selected for formality reasons only. However, any subject matter resulting from an intentional reference to any preceding claim (especially multiple dependencies) may be claimed as well, just as any combination of a claim and its features may be disclosed and claimed without regard to the dependencies selected in the appended claims. Subject matter that may be claimed includes not only combinations of features as recited in the appended claims, but also any other combinations of features within the scope of the claims, and each feature recited in the claims may be combined with any other feature or combination of features within the scope of the claims. Furthermore, any of the embodiments and features described or illustrated in this specification may be claimed in a separate claim and / or in any combination with any of the embodiments or features described or illustrated in this specification or with any of the features of the accompanying claims.

[0107] The scope of the present disclosure encompasses all changes, substitutions, variations, alterations, and modifications to the exemplary embodiments described or shown herein that would be understood by a person skilled in the art. The scope of the present disclosure is not limited to the exemplary embodiments described or shown herein. Moreover, although the present disclosure describes and shows each embodiment herein as including certain components, elements, features, functions, operations, or steps, any of these embodiments may include any combination or substitution of any of the components, elements, features, functions, operations, or steps described or shown anywhere herein that would be understood by a person skilled in the art. Moreover, references in the appended claims to an apparatus or system, or an apparatus or system component, being adapted to, arranged to, capable to, configured to, enabled to, operable to, or operative to perform a particular function encompass that apparatus, system, component, or particular function thereof, so long as the apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative, regardless of whether the apparatus, system, component, or particular function thereof is activated, turned on, or unlocked. Further, while the present disclosure describes or shows certain embodiments as providing certain advantages, a particular embodiment may provide none, some, or all of these advantages.

Claims

1. A method, which involves one or more computing devices, Receiving audio data associated with the patient, The aforementioned audio data is analyzed to quantify at least one audio variable, including a voiced pause variable, over a certain period of time. A method comprising determining an estimate of tau accumulation based on the quantified at least one speech variable.

2. The method according to claim 1, wherein receiving the voice data includes receiving an audio file containing an electronic recording of the patient's speech.

3. Analyzing the aforementioned audio data to quantify at least one audio variable is possible. The method according to claim 2, comprising analyzing the audio file and quantifying the at least one speech variable, including an acoustic speech variable.

4. Analyzing the aforementioned audio file to quantify the voiced pause variable is possible. The audio file is analyzed to identify pauses between words in the audio data, Identifying the aforementioned rest which is considered a voiced rest, Calculate the total number of pauses between words in the transcript and the total number of words in the transcript, Dividing the number of voiced rests by the total, The method according to claim 3, comprising returning the quantified voiced pause variable.

5. Determining the estimated value of the tau accumulation is Mapping one or more measurements of positron emission tomography (PET) tracer uptake by tau present in the brain of another subject to quantified speech variables of the other subject, The method according to claim 1, comprising estimating the amount of PET tracer uptake by tau predicted to be present in the brain of the patient, based on the mapping and the one or more quantified speech variables of the patient.

6. The method according to claim 1, further comprising classifying the patient as having a neurodegenerative disease based on the estimated value of the tau accumulation.

7. The method according to claim 1, further comprising sending a notification regarding the estimate of the tau accumulation to a clinician and / or a computing device associated with the patient.

8. In response to the determination of the estimated value of tau accumulation, therapeutic agents, symptomatic agents, neurologics, corticosteroids, and corticosteroids are selected from the group consisting of at least one compound comprising compounds against oxidative stress, anti-apoptotic compounds, metal chelators, DNA repair inhibitors, 3-amino-1-propanesulfonic acid (3APS), 1,3-propanedisulfonate (1,3PDS), secretase activators, beta- and gamma-secretase inhibitors, tau proteins, anti-tau antibodies, anti-tau agents, gene therapy drugs, neurotransmitters, beta-sheet disruptors, anti-inflammatory molecules, atypical antipsychotics, cholinesterase inhibitors, other drugs, and nutritional supplements. The method according to claim 1, further comprising generating a recommendation for the administration of a therapeutic agent selected from the group consisting of antibiotics, antiviral agents, anti-tau antibodies, tau inhibitors, anti-amyloid beta antibodies, beta-amyloid aggregation inhibitors, target-binding therapeutic agents, anti-BACE1 antibodies, BACE1 inhibitors, cholinesterase inhibitors, NMDA receptor antagonists, monoamine depletion agents, ergoloid mesylate, anticholinergic antiparkinsonist agents, dopaminergic antiparkinsonist agents, tetrabenazine, anti-inflammatory agents, hormones, vitamins, dimevorin, homotaurine, serotonin receptor activity modulators, interferon, and glucocorticoids.

9. A system including one or more computing devices, One or more non-temporary computer-readable storage media containing instructions, The system comprises one or more processors connected to one or more storage media, and the one or more processors Receiving audio data associated with the patient, Analyzing the aforementioned audio data to quantify at least one audio variable, including a voiced pause variable, over a certain period of time, and To determine an estimate of tau accumulation based on the quantified at least one of the aforementioned speech variables, A system configured to execute instructions for a purpose.

10. The system according to claim 9, wherein the command for receiving the voice data further includes a command for receiving an audio file containing an electronic recording of the patient's speech.

11. The command for analyzing the aforementioned audio data and quantifying at least one audio variable is: The system according to claim 10, further comprising instructions for analyzing the audio file and quantifying the at least one speech variable, including an acoustic speech variable.

12. The instruction for analyzing the audio file and quantifying the voiced pause variable is, The audio file is analyzed to identify pauses between words in the audio data. Identifying the aforementioned rest which is considered a voiced rest, Calculate the total number of pauses between words in the transcript and the total number of words in the transcript. Dividing the number of voiced rests by the total, To return the quantified voiced pause variable, The system according to claim 11, further comprising instructions for the following:

13. The instruction for determining the estimated value of the tau accumulation is, Mapping one or more measurements of positron emission tomography (PET) tracer uptake by tau present in the brain of another subject to quantified speech variables of the other subject, Based on the mapping and the one or more quantified speech variables of the patient, estimate the amount of PET tracer uptake by tau predicted to be present in the patient's brain. The system according to claim 9, further comprising instructions for the following:

14. The system according to claim 9, wherein the instruction further includes an instruction for classifying the patient as having a neurodegenerative disease based on an estimate of the tau accumulation.

15. The system according to claim 9, wherein the instruction further includes an instruction for transmitting a notification regarding an estimate of the tau accumulation to a computing device associated with a clinician and / or the patient.

16. The aforementioned instruction, In response to the determination of the estimated value of tau accumulation, therapeutic agents, symptomatic agents, neurologics, corticosteroids, anti- The system according to claim 9, further comprising instructions for generating recommendations for the administration of therapeutic agents selected from the group consisting of biomaterials, antiviral agents, anti-tau antibodies, tau inhibitors, anti-amyloid-beta antibodies, beta-amyloid aggregation inhibitors, target-binding therapeutic agents, anti-BACE1 antibodies, BACE1 inhibitors, cholinesterase inhibitors, NMDA receptor antagonists, monoamine depletion agents, ergoloid mesylate, anticholinergic antiparkinsonist agents, dopaminergic antiparkinsonist agents, tetrabenazine, anti-inflammatory agents, hormones, vitamins, dimevorin, homotaurine, serotonin receptor activity modulators, interferon, and glucocorticoids.

17. A method, which involves one or more computing devices, Receiving audio data associated with the patient, The aforementioned audio data is analyzed to quantify at least one audio variable, including a word frequency variable, over a certain period of time. A method comprising determining an estimate of tau accumulation based on the quantified at least one speech variable.

18. A system including one or more computing devices, One or more non-temporary computer-readable storage media containing instructions, The system comprises one or more processors connected to one or more storage media, and the one or more processors Receiving audio data associated with the patient, Analyzing the aforementioned audio data to quantify at least one audio variable, including a word frequency variable, over a certain period of time, and To determine an estimate of tau accumulation based on the quantified at least one of the aforementioned speech variables. A system configured to execute instructions for a purpose.

19. A method, which involves one or more computing devices, Receiving audio data associated with the patient, The aforementioned audio data is analyzed to quantify at least one audio variable, including a lexical diversity variable, over a certain period of time. A method comprising determining an estimate of tau accumulation based on the quantified at least one speech variable.

20. A system including one or more computing devices, One or more non-temporary computer-readable storage media containing instructions, The system comprises one or more processors connected to one or more storage media, and the one or more processors Receiving audio data associated with the patient, Analyzing the aforementioned audio data to quantify at least one audio variable, including a lexical diversity variable, over a certain period of time, and To determine an estimate of tau accumulation based on the quantified at least one of the aforementioned speech variables, A system configured to execute instructions for a purpose.

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