Výsledky vyhledávání - (automatic OR automatizace) machine learning algorithm based on (genetic OR genetics) algorithms

Zdroj: Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias

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12

Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms.

Autoři: Azouggagh, Lamiae Ibáñez-Escriche, Noelia Martínez-Álvaro, Marina a další

Zdroj: Animal Microbiome. 2/3/2025, Vol. 7 Issue 1, p1-15. 15p.

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13

Development of PI-ML Algorithm for Prediction of the Real-time Risk for Developing Pre-diabetes (PRAESIIDIUM)

Autoři: Spindox Labs The National Research Council, Italy The Institute of Electronics and Computer Sciences, Latvia a další

Přispěvatelé: Spindox Labs The National Research Council, Italy The Institute of Electronics and Computer Sciences, Latvia a další

Zdroj: Physics Informed Machine Learning-based Prediction and Reversion of Impaired Fasting Glucose Management
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Jolle A, Midthjell K, Holmen J, Carlsen SM, Tuomilehto J, Bjorngaard JH, Asvold BO. Validity of the FINDRISC as a prediction tool for diabetes in a contemporary Norwegian population: a 10-year follow-up of the HUNT study. BMJ Open Diabetes Res Care. 2019 Nov 28;7(1):e000769. doi: 10.1136/bmjdrc-2019-000769. eCollection 2019.
Tsalamandris S, Antonopoulos AS, Oikonomou E, Papamikroulis GA, Vogiatzi G, Papaioannou S, Deftereos S, Tousoulis D. The Role of Inflammation in Diabetes: Current Concepts and Future Perspectives. Eur Cardiol. 2019 Apr;14(1):50-59. doi: 10.15420/ecr.2018.33.1.
Castiglione F, Tieri P, De Graaf A, Franceschi C, Lio P, Van Ommen B, Mazza C, Tuchel A, Bernaschi M, Samson C, Colombo T, Castellani GC, Capri M, Garagnani P, Salvioli S, Nguyen VA, Bobeldijk-Pastorova I, Krishnan S, Cappozzo A, Sacchetti M, Morettini M, Ernst M. The onset of type 2 diabetes: proposal for a multi-scale model. JMIR Res Protoc. 2013 Oct 31;2(2):e44. doi: 10.2196/resprot.2854.
Palumbo MC, de Graaf AA, Morettini M, Tieri P, Krishnan S, Castiglione F. A computational model of the effects of macronutrients absorption and physical exercise on hormonal regulation and metabolic homeostasis. Comput Biol Med. 2023 Sep;163:107158. doi: 10.1016/j.compbiomed.2023.107158. Epub 2023 Jun 16.
Stolfi P, Valentini I, Palumbo MC, Tieri P, Grignolio A, Castiglione F. Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices. BMC Bioinformatics. 2020 Dec 14;21(Suppl 17):508. doi: 10.1186/s12859-020-03763-4.
Prana V, Tieri P, Palumbo MC, Mancini E, Castiglione F. Modeling the Effect of High Calorie Diet on the Interplay between Adipose Tissue, Inflammation, and Diabetes. Comput Math Methods Med. 2019 Feb 3;2019:7525834. doi: 10.1155/2019/7525834. eCollection 2019.
Palumbo MC, Morettini M, Tieri P, Diele F, Sacchetti M, Castiglione F. Personalizing physical exercise in a computational model of fuel homeostasis. PLoS Comput Biol. 2018 Apr 26;14(4):e1006073. doi: 10.1371/journal.pcbi.1006073. eCollection 2018 Apr.
Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021;3(6):422-440. doi:10.1038/s42254-021-00314-5
Zafar H, Channa A, Jeoti V, Stojanovic GM. Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring. Sensors (Basel). 2022 Jan 14;22(2):638. doi: 10.3390/s22020638.
Yao H, Shum AJ, Cowan M, Lahdesmaki I, Parviz BA. A contact lens with embedded sensor for monitoring tear glucose level. Biosens Bioelectron. 2011 Mar 15;26(7):3290-6. doi: 10.1016/j.bios.2010.12.042. Epub 2010 Dec 31.
Ellahham S. Artificial Intelligence: The Future for Diabetes Care. Am J Med. 2020 Aug;133(8):895-900. doi: 10.1016/j.amjmed.2020.03.033. Epub 2020 Apr 20.
Al-Shamsi S, Govender RD, King J. External validation and clinical usefulness of three commonly used cardiovascular risk prediction scores in an Emirati population: a retrospective longitudinal cohort study. BMJ Open. 2020 Oct 28;10(10):e040680. doi: 10.1136/bmjopen-2020-040680.
Rodrigues PM, Madeiro JP, Marques JAL. Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices. Bioengineering (Basel). 2023 Jul 2;10(7):792. doi: 10.3390/bioengineering10070792.
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14

2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project.

Autoři: Bianco, Rachele Coluccia, Sergio Marinoni, Michela a další

Zdroj: Nutrients. Jul2025, Vol. 18 Issue 13, p2196. 23p.

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15

Novel approaches to improve mosquito surveillance

Autoři: González Pérez, Maria

Thesis Advisors: Talavera Forcades, Sandra, Aranda Pallero, Carlos, Busquets Martí, Núria, Accensi Alemany, Francesc

Zdroj: TDX (Tesis Doctorals en Xarxa)

Témata: Mosquits, Mosquito, Mosquitos, Entomologia, Entomology, Entomología, Vectors, Vectores, Ciències Experimentals, Tecnologies

Popis souboru: application/pdf

Přístupová URL adresa: http://hdl.handle.net/10803/691224

View record in TDX

16

Development and Validation of a Novel Myocardial Infarction Prediction Model Based on ECG-AI

Přispěvatelé: Liu yong, Professor

Zdroj: Development and Validation of a Novel Myocardial Infarction Prediction Model Based on ECG-AI: A Multicenter Retrospective Cohort Study
Sadasivuni S, Saha M, Bhatia N, Banerjee I, Sanyal A. Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset. Sci Rep. 2022 Apr 5;12(1):5711. doi: 10.1038/s41598-022-09712-w.
Jawade P, Khillare KM, Mangudkar S, Palange A, Dhadwad J, Deshmukh M. A Comparative Study of Ischemia-Modified Albumin: A Promising Biomarker for Early Detection of Acute Coronary Syndrome (ACS). Cureus. 2023 Aug 30;15(8):e44357. doi: 10.7759/cureus.44357. eCollection 2023 Aug.
Swenne CA, Ter Haar CC. Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients. J Electrocardiol. 2024 Jan-Feb;82:34-41. doi: 10.1016/j.jelectrocard.2023.10.009. Epub 2023 Nov 7.
Akbilgic O, Butler L, Karabayir I, Chang PP, Kitzman DW, Alonso A, Chen LY, Soliman EZ. ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure. Eur Heart J Digit Health. 2021 Oct 9;2(4):626-634. doi: 10.1093/ehjdh/ztab080. eCollection 2021 Dec.
Aufiero S, Bleijendaal H, Robyns T, Vandenberk B, Krijger C, Bezzina C, Zwinderman AH, Wilde AAM, Pinto YM. A deep learning approach identifies new ECG features in congenital long QT syndrome. BMC Med. 2022 May 3;20(1):162. doi: 10.1186/s12916-022-02350-z.
Serhal H, Abdallah N, Marion JM, Chauvet P, Oueidat M, Humeau-Heurtier A. Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG. Comput Biol Med. 2022 Mar;142:105168. doi: 10.1016/j.compbiomed.2021.105168. Epub 2022 Jan 1.
Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis. Proc Natl Acad Sci U S A. 2021 Jun 15;118(24):e2020620118. doi: 10.1073/pnas.2020620118.
Liu YL, Lin CS, Cheng CC, Lin C. A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. J Pers Med. 2022 Jul 15;12(7):1150. doi: 10.3390/jpm12071150.
Pignolo RJ. AI-ECG and the Prediction of Accelerated Aging. Mayo Clin Proc. 2023 Apr;98(4):502-503. doi: 10.1016/j.mayocp.2023.02.016. No abstract available.
Wang X, Khurshid S, Choi SH, Friedman S, Weng LC, Reeder C, Pirruccello JP, Singh P, Lau ES, Venn R, Diamant N, Di Achille P, Philippakis A, Anderson CD, Ho JE, Ellinor PT, Batra P, Lubitz SA. Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms. Circ Genom Precis Med. 2023 Aug;16(4):340-349. doi: 10.1161/CIRCGEN.122.003808. Epub 2023 Jun 6.
Attia ZI, Friedman PA. Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning? Eur Heart J. 2023 Feb 21;44(8):693-695. doi: 10.1093/eurheartj/ehac733. No abstract available.
Martinez-Selles M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis. 2023 Apr 17;10(4):175. doi: 10.3390/jcdd10040175.
Butler L, Ivanov A, Celik T, Karabayir I, Chinthala L, Tootooni MS, Jaeger BC, Patterson LT, Doerr AJ, McManus DD, Davis RL, Herrington D, Akbilgic O. Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study. J Cardiovasc Dev Dis. 2024 Dec 8;11(12):395. doi: 10.3390/jcdd11120395.
Sekhri N, Feder GS, Junghans C, Hemingway H, Timmis AD. How effective are rapid access chest pain clinics? Prognosis of incident angina and non-cardiac chest pain in 8762 consecutive patients. Heart. 2007 Apr;93(4):458-63. doi: 10.1136/hrt.2006.090894. Epub 2006 Jun 21.
Stubbs P, Collinson P, Moseley D, Greenwood T, Noble M. Prospective study of the role of cardiac troponin T in patients admitted with unstable angina. BMJ. 1996 Aug 3;313(7052):262-4. doi: 10.1136/bmj.313.7052.262.
Farkouh ME, Smars PA, Reeder GS, Zinsmeister AR, Evans RW, Meloy TD, Kopecky SL, Allen M, Allison TG, Gibbons RJ, Gabriel SE. A clinical trial of a chest-pain observation unit for patients with unstable angina. Chest Pain Evaluation in the Emergency Room (CHEER) Investigators. N Engl J Med. 1998 Dec 24;339(26):1882-8. doi: 10.1056/NEJM199812243392603.
Fox KA, Goodman SG, Klein W, Brieger D, Steg PG, Dabbous O, Avezum A. Management of acute coronary syndromes. Variations in practice and outcome; findings from the Global Registry of Acute Coronary Events (GRACE). Eur Heart J. 2002 Aug;23(15):1177-89. doi: 10.1053/euhj.2001.3081.
Grech ED, Ramsdale DR. Acute coronary syndrome: unstable angina and non-ST segment elevation myocardial infarction. BMJ. 2003 Jun 7;326(7401):1259-61. doi: 10.1136/bmj.326.7401.1259. No abstract available.
Pollack CV Jr, Sites FD, Shofer FS, Sease KL, Hollander JE. Application of the TIMI risk score for unstable angina and non-ST elevation acute coronary syndrome to an unselected emergency department chest pain population. Acad Emerg Med. 2006 Jan;13(1):13-8. doi: 10.1197/j.aem.2005.06.031. Epub 2005 Dec 19.
Sinha MK, Roy D, Gaze DC, Collinson PO, Kaski JC. Role of "Ischemia modified albumin", a new biochemical marker of myocardial ischaemia, in the early diagnosis of acute coronary syndromes. Emerg Med J. 2004 Jan;21(1):29-34. doi: 10.1136/emj.2003.006007.
de Beer FC, Hind CR, Fox KM, Allan RM, Maseri A, Pepys MB. Measurement of serum C-reactive protein concentration in myocardial ischaemia and infarction. Br Heart J. 1982 Mar;47(3):239-43. doi: 10.1136/hrt.47.3.239.
Katus HA, Remppis A, Neumann FJ, Scheffold T, Diederich KW, Vinar G, Noe A, Matern G, Kuebler W. Diagnostic efficiency of troponin T measurements in acute myocardial infarction. Circulation. 1991 Mar;83(3):902-12. doi: 10.1161/01.cir.83.3.902.
Yagi R, Goto S, Himeno Y, Katsumata Y, Hashimoto M, MacRae CA, Deo RC. Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms. Nat Commun. 2024 Mar 21;15(1):2536. doi: 10.1038/s41467-024-45733-x.
Shen CP, Muse ED. Towards an artificial intelligence-augmented, ECG-enabled physical exam. Lancet Digit Health. 2022 Feb;4(2):e78-e79. doi: 10.1016/S2589-7500(21)00281-8. Epub 2022 Jan 5. No abstract available.
Lincoff AM, Brown-Frandsen K, Colhoun HM, Deanfield J, Emerson SS, Esbjerg S, Hardt-Lindberg S, Hovingh GK, Kahn SE, Kushner RF, Lingvay I, Oral TK, Michelsen MM, Plutzky J, Tornoe CW, Ryan DH; SELECT Trial Investigators. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N Engl J Med. 2023 Dec 14;389(24):2221-2232. doi: 10.1056/NEJMoa2307563. Epub 2023 Nov 11.
Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Asselbergs FW, Brant LCC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. Eur Heart J. 2025 Mar 13;46(11):1044-1053. doi: 10.1093/eurheartj/ehae914.
Biscaglia S, Guiducci V, Escaned J, Moreno R, Lanzilotti V, Santarelli A, Cerrato E, Sacchetta G, Jurado-Roman A, Menozzi A, Amat Santos I, Diez Gil JL, Ruozzi M, Barbierato M, Fileti L, Picchi A, Lodolini V, Biondi-Zoccai G, Maietti E, Pavasini R, Cimaglia P, Tumscitz C, Erriquez A, Penzo C, Colaiori I, Pignatelli G, Casella G, Iannopollo G, Menozzi M, Varbella F, Caretta G, Dudek D, Barbato E, Tebaldi M, Campo G; FIRE Trial Investigators. Complete or Culprit-Only PCI in Older Patients with Myocardial Infarction. N Engl J Med. 2023 Sep 7;389(10):889-898. doi: 10.1056/NEJMoa2300468. Epub 2023 Aug 26.
Kromhout D, Giltay EJ, Geleijnse JM; Alpha Omega Trial Group. n-3 fatty acids and cardiovascular events after myocardial infarction. N Engl J Med. 2010 Nov 18;363(21):2015-26. doi: 10.1056/NEJMoa1003603. Epub 2010 Aug 28.
Dupulthys S, Dujardin K, Anne W, Pollet P, Vanhaverbeke M, McAuliffe D, Lammertyn PJ, Berteloot L, Mertens N, De Jaeger P. Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm. Europace. 2024 Feb 1;26(2):euad354. doi: 10.1093/europace/euad354.
Nechita LC, Nechita A, Voipan AE, Voipan D, Debita M, Fulga A, Fulga I, Musat CL. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics (Basel). 2024 Aug 23;14(17):1839. doi: 10.3390/diagnostics14171839.
Chen HY, Lin CS, Fang WH, Lee CC, Ho CL, Wang CH, Lin C. Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of Long-Term Cardiovascular Outcome in Patients With Normal Ejection Fraction. Front Med (Lausanne). 2022 Apr 11;9:870523. doi: 10.3389/fmed.2022.870523. eCollection 2022.
Lee HS, Kang S, Jo YY, Son JM, Lee MS, Kwon JM, Kim KH. AI-Enabled Smartwatch ECG: A Feasibility Study for Early Prediction and Prevention of Heart Failure Rehospitalization. JACC Basic Transl Sci. 2025 Mar;10(3):250-252. doi: 10.1016/j.jacbts.2025.01.005. Epub 2025 Feb 11. No abstract available.
Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al-Alusi MA, Sarma G, Foulkes AS, Ellinor PT, Anderson CD, Ho JE, Philippakis AA, Batra P, Lubitz SA. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2022 Jan 11;145(2):122-133. doi: 10.1161/CIRCULATIONAHA.121.057480. Epub 2021 Nov 8.

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17

Deep-learning-based buffalo identification through muzzle pattern images.

Autoři: Ermetin, Orhan Örnek, Humar Kahramanlı

Zdroj: Archives Animal Breeding / Archiv Tierzucht. 2025, Vol. 68 Issue 3, p473-484. 12p.

Témata: Artificial intelligence, Water buffalo, Artificial neural networks, Biometric identification, Livestock, Convolutional neural networks, Deep learning, Identification of animals

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18

Multi-observational Study for the Early Identification of Autism Spectrum Disorder (ASD) and Other Neurodevelopmental Disorders (NDD) in At-risk Populations (BABY@NET)

Autoři: Associazione La Nostra Famiglia, Como, Italy Federico II University Fondazione Policlinico Universitario Agostino Gemelli IRCCS a další

Zdroj: BABY@NET: A Technology-based National Surveillance Network for the Early Identification of Autism Spectrum Disorder and Other Neurodevelopmental Disorders in At-risk Populations
Caruso A, Micai M, Gila L, Fulceri F, Scattoni ML. The Italian Network for Early Detection of Autism Spectrum Disorder: Research Activities and National Policies. Psychiatr Danub. 2021 Dec;33(Suppl 11):65-68.
Fontana C, Marasca F, Provitera L, Mancinelli S, Pesenti N, Sinha S, Passera S, Abrignani S, Mosca F, Lodato S, Bodega B, Fumagalli M. Early maternal care restores LINE-1 methylation and enhances neurodevelopment in preterm infants. BMC Med. 2021 Feb 5;19(1):42. doi: 10.1186/s12916-020-01896-0.
Riva V, Cantiani C, Mornati G, Gallo M, Villa L, Mani E, Saviozzi I, Marino C, Molteni M. Distinct ERP profiles for auditory processing in infants at-risk for autism and language impairment. Sci Rep. 2018 Jan 15;8(1):715. doi: 10.1038/s41598-017-19009-y.
Marchi V, Hakala A, Knight A, D'Acunto F, Scattoni ML, Guzzetta A, Vanhatalo S. Automated pose estimation captures key aspects of General Movements at eight to 17 weeks from conventional videos. Acta Paediatr. 2019 Oct;108(10):1817-1824. doi: 10.1111/apa.14781. Epub 2019 Apr 1.
Caruso A, Gila L, Fulceri F, Salvitti T, Micai M, Baccinelli W, Bulgheroni M, Scattoni ML. Early Motor Development Predicts Clinical Outcomes of Siblings at High-Risk for Autism: Insight from an Innovative Motion-Tracking Technology. Brain Sci. 2020 Jun 16;10(6):379. doi: 10.3390/brainsci10060379.
Purpura G, Costanzo V, Chericoni N, Puopolo M, Scattoni ML, Muratori F, Apicella F. Bilateral Patterns of Repetitive Movements in 6- to 12-Month-Old Infants with Autism Spectrum Disorders. Front Psychol. 2017 Jul 11;8:1168. doi: 10.3389/fpsyg.2017.01168. eCollection 2017.
Loke YJ, Hannan AJ, Craig JM. The Role of Epigenetic Change in Autism Spectrum Disorders. Front Neurol. 2015 May 26;6:107. doi: 10.3389/fneur.2015.00107. eCollection 2015.
Needham BD, Adame MD, Serena G, Rose DR, Preston GM, Conrad MC, Campbell AS, Donabedian DH, Fasano A, Ashwood P, Mazmanian SK. Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry. 2021 Mar 1;89(5):451-462. doi: 10.1016/j.biopsych.2020.09.025. Epub 2020 Oct 10.
Baccinelli W, Bulgheroni M, Simonetti V, Fulceri F, Caruso A, Gila L, Scattoni ML. Movidea: A Software Package for Automatic Video Analysis of Movements in Infants at Risk for Neurodevelopmental Disorders. Brain Sci. 2020 Mar 31;10(4):203. doi: 10.3390/brainsci10040203.
Malatesta G, Marzoli D, Apicella F, Abiuso C, Muratori F, Forrester GS, Vallortigara G, Scattoni ML, Tommasi L. Received Cradling Bias During the First Year of Life: A Retrospective Study on Children With Typical and Atypical Development. Front Psychiatry. 2020 Feb 27;11:91. doi: 10.3389/fpsyt.2020.00091. eCollection 2020.
Chericoni N, Balboni G, Costanzo V, Mancini A, Prosperi M, Lasala R, Tancredi R, Scattoni ML; NIDA Network; Muratori F, Apicella F. A Combined Study on the Use of the Child Behavior Checklist 1(1/2)-5 for Identifying Autism Spectrum Disorders at 18 Months. J Autism Dev Disord. 2021 Nov;51(11):3829-3842. doi: 10.1007/s10803-020-04838-0. Epub 2021 Jan 4.
Frye RE, Vassall S, Kaur G, Lewis C, Karim M, Rossignol D. Emerging biomarkers in autism spectrum disorder: a systematic review. Ann Transl Med. 2019 Dec;7(23):792. doi: 10.21037/atm.2019.11.53.
Micai M, Fulceri F, Caruso A, Guzzetta A, Gila L, Scattoni ML. Early behavioral markers for neurodevelopmental disorders in the first 3 years of life: An overview of systematic reviews. Neurosci Biobehav Rev. 2020 Sep;116:183-201. doi: 10.1016/j.neubiorev.2020.06.027. Epub 2020 Jun 28.
Di Giorgio E, Rosa-Salva O, Frasnelli E, Calcagni A, Lunghi M, Scattoni ML, Simion F, Vallortigara G. Abnormal visual attention to simple social stimuli in 4-month-old infants at high risk for Autism. Sci Rep. 2021 Aug 4;11(1):15785. doi: 10.1038/s41598-021-95418-4.
Zeidan J, Fombonne E, Scorah J, Ibrahim A, Durkin MS, Saxena S, Yusuf A, Shih A, Elsabbagh M. Global prevalence of autism: A systematic review update. Autism Res. 2022 May;15(5):778-790. doi: 10.1002/aur.2696. Epub 2022 Mar 3.