JP7090184B2 - 人工知能ディープラーニング学習を利用した生体測定物濃度測定方法 - Google Patents
人工知能ディープラーニング学習を利用した生体測定物濃度測定方法 Download PDFInfo
- Publication number
- JP7090184B2 JP7090184B2 JP2020572819A JP2020572819A JP7090184B2 JP 7090184 B2 JP7090184 B2 JP 7090184B2 JP 2020572819 A JP2020572819 A JP 2020572819A JP 2020572819 A JP2020572819 A JP 2020572819A JP 7090184 B2 JP7090184 B2 JP 7090184B2
- Authority
- JP
- Japan
- Prior art keywords
- learning
- artificial intelligence
- deep learning
- concentration
- substance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000013135 deep learning Methods 0.000 title claims description 79
- 238000013473 artificial intelligence Methods 0.000 title claims description 72
- 238000000691 measurement method Methods 0.000 title claims description 15
- 238000000034 method Methods 0.000 claims description 75
- 210000004369 blood Anatomy 0.000 claims description 64
- 239000008280 blood Substances 0.000 claims description 64
- 238000004422 calculation algorithm Methods 0.000 claims description 61
- 239000000126 substance Substances 0.000 claims description 41
- 238000013528 artificial neural network Methods 0.000 claims description 38
- 238000005259 measurement Methods 0.000 claims description 29
- 230000002452 interceptive effect Effects 0.000 claims description 14
- 239000000523 sample Substances 0.000 claims description 14
- 239000012472 biological sample Substances 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000008859 change Effects 0.000 claims description 11
- 230000006870 function Effects 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 11
- 230000002159 abnormal effect Effects 0.000 claims description 10
- 230000005856 abnormality Effects 0.000 claims description 8
- 230000027756 respiratory electron transport chain Effects 0.000 claims description 8
- 238000005457 optimization Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 230000004913 activation Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000006479 redox reaction Methods 0.000 claims description 6
- 238000000611 regression analysis Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 5
- 238000013527 convolutional neural network Methods 0.000 claims description 4
- 238000003487 electrochemical reaction Methods 0.000 claims description 4
- 108090000854 Oxidoreductases Proteins 0.000 claims description 3
- 102000004316 Oxidoreductases Human genes 0.000 claims description 3
- 210000004027 cell Anatomy 0.000 claims description 3
- 239000003153 chemical reaction reagent Substances 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 210000002569 neuron Anatomy 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000006276 transfer reaction Methods 0.000 claims description 3
- 238000003556 assay Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 claims description 2
- 238000007620 mathematical function Methods 0.000 claims description 2
- 230000000306 recurrent effect Effects 0.000 claims description 2
- 239000013076 target substance Substances 0.000 claims 2
- 230000001174 ascending effect Effects 0.000 claims 1
- 238000011109 contamination Methods 0.000 claims 1
- 230000000977 initiatory effect Effects 0.000 claims 1
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 52
- 239000008103 glucose Substances 0.000 description 52
- 238000005534 hematocrit Methods 0.000 description 11
- 241000894007 species Species 0.000 description 8
- 102000004190 Enzymes Human genes 0.000 description 6
- 108090000790 Enzymes Proteins 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 6
- 229940088598 enzyme Drugs 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000007613 environmental effect Effects 0.000 description 5
- 210000003743 erythrocyte Anatomy 0.000 description 5
- 238000007781 pre-processing Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 239000000284 extract Substances 0.000 description 4
- 229930195712 glutamate Natural products 0.000 description 4
- WHBMMWSBFZVSSR-UHFFFAOYSA-N 3-hydroxybutyric acid Chemical compound CC(O)CC(O)=O WHBMMWSBFZVSSR-UHFFFAOYSA-N 0.000 description 3
- 108010050375 Glucose 1-Dehydrogenase Proteins 0.000 description 3
- WHUUTDBJXJRKMK-VKHMYHEASA-N L-glutamic acid Chemical compound OC(=O)[C@@H](N)CCC(O)=O WHUUTDBJXJRKMK-VKHMYHEASA-N 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000012417 linear regression Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- LJCNDNBULVLKSG-UHFFFAOYSA-N 2-aminoacetic acid;butane Chemical compound CCCC.CCCC.NCC(O)=O LJCNDNBULVLKSG-UHFFFAOYSA-N 0.000 description 2
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Natural products OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 description 2
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 2
- 239000004366 Glucose oxidase Substances 0.000 description 2
- 241000282412 Homo Species 0.000 description 2
- MHAJPDPJQMAIIY-UHFFFAOYSA-N Hydrogen peroxide Chemical compound OO MHAJPDPJQMAIIY-UHFFFAOYSA-N 0.000 description 2
- JVTAAEKCZFNVCJ-UHFFFAOYSA-M Lactate Chemical compound CC(O)C([O-])=O JVTAAEKCZFNVCJ-UHFFFAOYSA-M 0.000 description 2
- 235000010323 ascorbic acid Nutrition 0.000 description 2
- 229960005070 ascorbic acid Drugs 0.000 description 2
- 239000011668 ascorbic acid Substances 0.000 description 2
- 235000012000 cholesterol Nutrition 0.000 description 2
- DDRJAANPRJIHGJ-UHFFFAOYSA-N creatinine Chemical compound CN1CC(=O)NC1=N DDRJAANPRJIHGJ-UHFFFAOYSA-N 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 229940116332 glucose oxidase Drugs 0.000 description 2
- 235000019420 glucose oxidase Nutrition 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- TYQCGQRIZGCHNB-JLAZNSOCSA-N l-ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(O)=C(O)C1=O TYQCGQRIZGCHNB-JLAZNSOCSA-N 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 239000002207 metabolite Substances 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 238000011002 quantification Methods 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 102000007698 Alcohol dehydrogenase Human genes 0.000 description 1
- 108010021809 Alcohol dehydrogenase Proteins 0.000 description 1
- 108010025188 Alcohol oxidase Proteins 0.000 description 1
- 101000950981 Bacillus subtilis (strain 168) Catabolic NAD-specific glutamate dehydrogenase RocG Proteins 0.000 description 1
- 108010089254 Cholesterol oxidase Proteins 0.000 description 1
- 108010015776 Glucose oxidase Proteins 0.000 description 1
- 102000016901 Glutamate dehydrogenase Human genes 0.000 description 1
- 108010073450 Lactate 2-monooxygenase Proteins 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 102000001494 Sterol O-Acyltransferase Human genes 0.000 description 1
- 108010054082 Sterol O-acyltransferase Proteins 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- WQZGKKKJIJFFOK-VFUOTHLCSA-N beta-D-glucose Chemical compound OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-VFUOTHLCSA-N 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 229940109239 creatinine Drugs 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 239000012502 diagnostic product Substances 0.000 description 1
- -1 external environment Substances 0.000 description 1
- 238000000338 in vitro Methods 0.000 description 1
- 150000002576 ketones Chemical class 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
- G01N15/0656—Investigating concentration of particle suspensions using electric, e.g. electrostatic methods or magnetic methods
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/66—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood sugars, e.g. galactose
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/28—Electrolytic cell components
- G01N27/30—Electrodes, e.g. test electrodes; Half-cells
- G01N27/327—Biochemical electrodes, e.g. electrical or mechanical details for in vitro measurements
- G01N27/3271—Amperometric enzyme electrodes for analytes in body fluids, e.g. glucose in blood
- G01N27/3274—Corrective measures, e.g. error detection, compensation for temperature or hematocrit, calibration
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/483—Physical analysis of biological material
- G01N33/487—Physical analysis of biological material of liquid biological material
- G01N33/48707—Physical analysis of biological material of liquid biological material by electrical means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
- G06F18/2193—Validation; Performance evaluation; Active pattern learning techniques based on specific statistical tests
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/698—Matching; Classification
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/54—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving glucose or galactose
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Hematology (AREA)
- Data Mining & Analysis (AREA)
- Immunology (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Pathology (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Urology & Nephrology (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Electrochemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Evolutionary Biology (AREA)
- Biotechnology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Medical Informatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Dispersion Chemistry (AREA)
- Multimedia (AREA)
- Probability & Statistics with Applications (AREA)
- Diabetes (AREA)
- Cell Biology (AREA)
Description
1)特定の階段化されたはしご形の山および谷電圧付近での感応電流
2)階段化されたはしご形において各階段の感応電流からなる曲線の曲率
3)階段化されたはしご形の山での電流値と谷での電流値との差
4)上りと下りとの中間の階段化されたはしご形での感応電流
5)各階段化されたはしご形サイクルの開始および終了地点での感応電流
6)階段化されたはしご形波から得た感応電流の平均値
7)前記1乃至6のフィーチャから得た電流値を四則演算、指数、ログ、三角関数などの数学的関数で表現して得ることができる値
温度Featureを含むか否かによるニューラルネットアルゴリズム性能変化を観測するために同一の構造がニューラルネットアルゴリズムで、温度Featurを含む/含まずに2個の同一の人工知能ディープラーニングアルゴリズムモデルを具現して[表1]および図7に示した。
アルゴリズムに適用される温度Featureの値は、メーターに付着された温度センサーを通じて得られる値であって、急激な周囲環境の変化が生じる時に即時正確な温度値を測定し難く、温度平衡がなされるための時間が必要である。
Claims (9)
- 人工知能ディープラーニング学習を利用した生体測定物濃度測定方法において、
分析対象物質の酸化還元反応を触媒することができる酸化還元酵素と電子伝達媒介体が固定されており、作動電極と補助電極を備えた試料セルに血液サンプルを注入する段階と、
前記分析対象物質の酸化還元反応を開始し、電子伝達反応を進行させることができるように前記作動電極に一定の直流電圧を印加して少なくとも一時点以上の特徴点で第1感応電流を得る段階と、
前記一定の直流電圧を印加後、Λ模様の階段化されたはしご形摂動電圧を印加して少なくとも2時点以上で第2感応電流を得る段階と、
前記第1感応電流または前記第2感応電流から予め定められたフィーチャ(feature)を計算する段階と、
前記生体試料内の少なくとも1以上の妨害物質の影響が最小となるように人工知能ディープラーニング学習により少なくとも1以上のフィーチャ(feature)関数で構成された検定式を用いて、補正された前記分析対象物質の濃度を出力する段階とを含み、
前記補正された前記分析対象物質の濃度を出力する段階は、人工知能ディープラーニング学習により、前記第1感応電流と前記第2感応電流を再び得て新しいフィーチャを計算する段階を含み、
前記人工知能ディープラーニング学習は、複数の異なる実験条件の血液サンプルを製作し、該血液サンプルの濃度をそれぞれ測定することを通じて、アルゴリズムを作るための学習データを得る段階であって、前記得られた学習データは、1次元時系列データで時間の流れに応じた測定物の電気化学的反応を示す段階と、
前記学習データの正規化(Normalization)あるいは標準化(Standardization)を通じてデータを一定の大きさ(scale)あるいは分布(distribution)に変換する段階と、
前記変換されたデータを、多チャンネルデータを組み合わせたりドメイン転換して信号処理する段階と、
人工神経網ディープラーニング技術を利用して入力による適した結果を出力することができるアルゴリズムを学習する段階とを含む人工知能ディープラーニング学習を利用した生体測定物濃度測定方法。 - 前記人工知能ディープラーニング学習により前記第1または第2感応電流で前記分析対象物質と妨害物質に対する線形依存性が異なる特徴点を選択し、前記特徴点でフィーチャを構成し、前記フィーチャで構成した検定式を作って前記特徴点で前記フィーチャを作る方法は、特定の階段化されたはしご形の山および谷電圧付近の第2感応電流、前記階段化されたはしご形摂動電圧で各階段の感応電流からなる曲線の曲率、前記階段化されたはしご形摂動電圧の山での電流値と谷での電流値との差、上りと下りの中間の階段化されたはしご形摂動電圧での感応電流、各階段化されたはしご形摂動電圧のサイクルの開始および終了地点での感応電流、および階段化されたはしご形摂動電圧で得た感応電流の平均値のうちの一つを使用するか、またはこれから得た電流値を四則演算、指数、ログ、三角関数などの数学的関数で表現して得ることができる値を使用する、請求項1に記載の人工知能ディープラーニング学習を利用した生体測定物濃度測定方法。
- 前記第2感応電流は、第1感応電流を得た後、0.1乃至1秒以内に得られる、請求項1に記載の人工知能ディープラーニング学習を利用した生体測定物濃度測定方法。
- 前記人工知能ディープラーニング学習は、前記生体試料内の分析対象物質の濃度異常、前記生体試料内の分析対象物質の汚染、前記生体試料内の分析対象物質が入っているストリップの不正使用、周辺温度、電極の材質、電極の配列方式、流路の模様、使用する試薬の特性、生体試料内の分析対象物質の濃度測定装置の異常のうちの少なくとも2以上の干渉要因を補正する、請求項1に記載の人工知能ディープラーニング学習を利用した生体測定物濃度測定方法。
- 前記周辺温度が変化した場合にも温度バランシングのための待機時間なしに人工知能ディープラーニング学習により前記生体試料内の分析対象物質の濃度測定を補正する、請求項4に記載の人工知能ディープラーニング学習を利用した生体測定物濃度測定方法。
- 前記人工神経網内の多くの層に存在するニューロン間の加重値を調節する段階を含み、前記人工神経網は、構造によりConvolutional Neural Network(CNN)、Deep Belief Networks(DBN)、そしてRecurrent Neural Networks(RNNs)のうちのいずれか一つを利用してフィーチャを自動抽出する段階を含む、請求項1に記載の人工知能ディープラーニング学習を利用した生体測定物濃度測定方法。
- 前記フィーチャを自動抽出する段階は、Restricted Boltzmann machines(RBM)を利用し、入力データ分布と確率により決定(stochastic decision)される再構成データの分布が類似に作る最適化段階を含む、請求項6に記載の人工知能ディープラーニング学習を活用した測定物濃度測定方法。
- 前記最適化段階は、人工神経網全体の加重値(Weight)とバイアス(Bias)値を決定する段階と、活性化関数を利用する段階とを含む、請求項7に記載の人工知能ディープラーニング学習を活用した測定物濃度測定方法。
- 前記人工神経網は、出力層の活性化関数あるいは構造変更を通じてデータの種類(Type)を分類する分類器あるいは値を推定する回帰分析(Regression)に使用する、請求項8に記載の人工知能ディープラーニング学習を活用した測定物濃度測定方法。
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR10-2018-0080372 | 2018-07-11 | ||
KR1020180080372A KR102095959B1 (ko) | 2018-07-11 | 2018-07-11 | 인공지능 딥러닝 학습을 이용한 생체측정물 농도 측정방법 |
PCT/KR2018/007934 WO2020013361A1 (ko) | 2018-07-11 | 2018-07-13 | 인공지능 딥러닝 학습을 이용한 생체측정물 농도 측정방법 |
Publications (2)
Publication Number | Publication Date |
---|---|
JP2021529315A JP2021529315A (ja) | 2021-10-28 |
JP7090184B2 true JP7090184B2 (ja) | 2022-06-23 |
Family
ID=69142371
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2020572819A Active JP7090184B2 (ja) | 2018-07-11 | 2018-07-13 | 人工知能ディープラーニング学習を利用した生体測定物濃度測定方法 |
Country Status (9)
Country | Link |
---|---|
US (1) | US20210285909A1 (ja) |
EP (1) | EP3822625B1 (ja) |
JP (1) | JP7090184B2 (ja) |
KR (1) | KR102095959B1 (ja) |
CN (1) | CN112105923B (ja) |
DK (1) | DK3822625T3 (ja) |
ES (1) | ES2960485T3 (ja) |
HU (1) | HUE063277T2 (ja) |
WO (1) | WO2020013361A1 (ja) |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR102433251B1 (ko) * | 2020-11-03 | 2022-08-18 | (주)아이티공간 | 제품의 실시간 품질 검사방법 |
CN112666098A (zh) * | 2020-11-06 | 2021-04-16 | 上海市第八人民医院 | 夏季肠道传染病致病病原体检测系统 |
KR102510099B1 (ko) * | 2021-07-01 | 2023-03-14 | (주)아이티공간 | 딥러닝을 이용한 기기의 예지 보전방법 |
KR102510101B1 (ko) * | 2021-07-01 | 2023-03-14 | (주)아이티공간 | 딥러닝을 이용한 기기의 예지 보전방법 |
KR20230028896A (ko) * | 2021-08-23 | 2023-03-03 | 경북대학교 산학협력단 | 인코딩 자성입자의 딥러닝 기반 디코딩을 이용한 시료의 다중검출 분석 장치 및 그 방법 |
KR102700498B1 (ko) * | 2021-10-20 | 2024-08-30 | 주식회사 시노펙스 | 휴대용 단말기를 이용한 혈액 분석 시스템 |
US20240192193A1 (en) * | 2022-12-09 | 2024-06-13 | Orange Biomed Ltd., Co | Sample-testing system for measuring properties of red blood cells |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2016061772A (ja) | 2014-09-17 | 2016-04-25 | アイセンス,インコーポレーテッド | 生体試料内の分析対象物質の濃度測定方法および測定装置 |
Family Cites Families (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4897162A (en) * | 1986-11-14 | 1990-01-30 | The Cleveland Clinic Foundation | Pulse voltammetry |
SE524574C2 (sv) * | 2002-12-09 | 2004-08-31 | Otre Ab | Metod för signalbehandling för voltammetri |
CN1930490A (zh) * | 2003-11-25 | 2007-03-14 | 麦卡利斯有限公司 | 目标探测方法和设备 |
US7699973B2 (en) * | 2006-06-30 | 2010-04-20 | Abbott Diabetes Care Inc. | Rapid analyte measurement assay |
US7822557B2 (en) * | 2006-10-31 | 2010-10-26 | Abbott Diabetes Care Inc. | Analyte sensors and methods |
US8343331B2 (en) * | 2007-09-27 | 2013-01-01 | Philosys Co., Ltd. | Method for correcting erroneous results of measurement in biosensors and apparatus using the same |
CA2742149C (en) * | 2008-11-28 | 2016-07-05 | Panasonic Corporation | Sensor chip, biosensor system, method for measuring temperature of biological sample, method for measuring temperature of blood sample, and method for measuring concentration of analyte in blood sample |
US8709232B2 (en) * | 2012-04-30 | 2014-04-29 | Cilag Gmbh International | Analyte measurement technique and system |
KR101357134B1 (ko) * | 2012-05-23 | 2014-02-05 | 주식회사 아이센스 | 전기화학적 바이오센서, 휴대용 계측기 및 이들을 사용한 혈액시료 중 분석대상물질의 농도 측정방법 |
WO2016043361A1 (ko) * | 2014-09-17 | 2016-03-24 | 주식회사 아이센스 | 생체시료 내 분석대상물질의 농도측정방법 및 측정장치 |
KR20160096460A (ko) * | 2015-02-05 | 2016-08-16 | 삼성전자주식회사 | 복수의 분류기를 포함하는 딥 러닝 기반 인식 시스템 및 그 제어 방법 |
CN113421652B (zh) * | 2015-06-02 | 2024-06-28 | 推想医疗科技股份有限公司 | 对医疗数据进行分析的方法、训练模型的方法及分析仪 |
KR101740034B1 (ko) * | 2015-07-20 | 2017-06-08 | 주식회사 아이센스 | 베타-히드록시부틸레이트 검출을 위한 산화환원 시약 조성물, 및 상기시약 조성물을 포함하는 전기화학적 바이오센서 |
KR102655736B1 (ko) * | 2016-07-19 | 2024-04-05 | 삼성전자주식회사 | 이종 스펙트럼 기반 혈당 추정 장치 및 방법 |
CN107192690B (zh) * | 2017-05-19 | 2019-04-23 | 重庆大学 | 近红外光谱无创血糖检测方法及其检测网络模型训练方法 |
CN107908928A (zh) * | 2017-12-21 | 2018-04-13 | 天津科技大学 | 一种基于深度学习技术的血红蛋白动态光谱分析预测方法 |
-
2018
- 2018-07-11 KR KR1020180080372A patent/KR102095959B1/ko active IP Right Grant
- 2018-07-13 CN CN201880093318.5A patent/CN112105923B/zh active Active
- 2018-07-13 HU HUE18925936A patent/HUE063277T2/hu unknown
- 2018-07-13 WO PCT/KR2018/007934 patent/WO2020013361A1/ko unknown
- 2018-07-13 DK DK18925936.9T patent/DK3822625T3/da active
- 2018-07-13 US US17/257,545 patent/US20210285909A1/en active Pending
- 2018-07-13 EP EP18925936.9A patent/EP3822625B1/en active Active
- 2018-07-13 ES ES18925936T patent/ES2960485T3/es active Active
- 2018-07-13 JP JP2020572819A patent/JP7090184B2/ja active Active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2016061772A (ja) | 2014-09-17 | 2016-04-25 | アイセンス,インコーポレーテッド | 生体試料内の分析対象物質の濃度測定方法および測定装置 |
Also Published As
Publication number | Publication date |
---|---|
KR102095959B1 (ko) | 2020-04-01 |
CN112105923A (zh) | 2020-12-18 |
HUE063277T2 (hu) | 2024-01-28 |
EP3822625A1 (en) | 2021-05-19 |
DK3822625T3 (da) | 2024-01-02 |
EP3822625A4 (en) | 2021-08-25 |
US20210285909A1 (en) | 2021-09-16 |
CN112105923B (zh) | 2024-01-09 |
WO2020013361A1 (ko) | 2020-01-16 |
EP3822625B1 (en) | 2023-09-27 |
KR20200006695A (ko) | 2020-01-21 |
ES2960485T3 (es) | 2024-03-05 |
JP2021529315A (ja) | 2021-10-28 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP7090184B2 (ja) | 人工知能ディープラーニング学習を利用した生体測定物濃度測定方法 | |
JP7125493B2 (ja) | 人工神経網ディープラーニング技法を活用した測定物分析方法、装置、学習方法およびシステム | |
US7018843B2 (en) | Instrument | |
CN105738430B (zh) | 生物样品内待分析物质的浓度检测方法及检测装置 | |
TWI825240B (zh) | 用於判定體液樣品中之分析物濃度的方法和系統及用於產生軟體實施模組的方法和系統 | |
JP2005515413A (ja) | 電気化学的な信号処理方法及び装置 | |
Johnson et al. | Failure of standard training sets in the analysis of fast-scan cyclic voltammetry data | |
Keithley et al. | Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data | |
Pigani et al. | Pedot modified electrodes in amperometric sensing for analysis of red wine samples | |
CN104870982B (zh) | 用于评估医学测量曲线的方法 | |
CN104921736A (zh) | 一种包含参数估计功能滤波模块的连续血糖监测设备 | |
CN110308713A (zh) | 一种基于k近邻重构的工业过程故障变量识别方法 | |
Głowacz et al. | Comparison of various data analysis techniques applied for the classification of oligopeptides and amino acids by voltammetric electronic tongue | |
JP4635220B2 (ja) | 複数の化学物質の測定方法 | |
Olarte et al. | Measurement and characterization of glucose in NaCl aqueous solutions by electrochemical impedance spectroscopy | |
JP2006153548A (ja) | 被検物質の定量方法 | |
CA2970194A1 (en) | Analyte measurement | |
KR20240135184A (ko) | Fscv 데이터 기반 다양한 신경전달물질의 장시간 농도 측정 결과를 동시에 제공하는 딥러닝 방식의 신경전달물질 농도 측정 장치 및 방법 | |
Braga et al. | Using artificial neural nets to Hemo metabolites identification | |
Braga et al. | A Portable and Low Cost System to Blood Glucose, Cholesterol and Urea Identification | |
CN114295704A (zh) | 一种基于特征参数提取的微浓度梯度溶液电化学测定方法 | |
Keithley | Improving In Vivo Fast-Scan Cyclic Voltammetric Detection of Neuromodulators | |
Braga et al. | An Artificial Neural Network System to Blood Metabolites Identification |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20201225 |
|
A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20220204 |
|
A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20220506 |
|
TRDD | Decision of grant or rejection written | ||
A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20220520 |
|
A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20220613 |
|
R150 | Certificate of patent or registration of utility model |
Ref document number: 7090184 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |