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Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer

by Miao, Ruizhong, Badger, Taylor C., Groesch, Kathleen, Diaz-Sylvester, Paula L., Wilson, Teresa, Ghareeb, Allen, Martin, Jongjin Anne, Cregger, Melissa, Welge, Michael, Bushell, Colleen, Auvil, Loretta, Zhu, Ruoqing, Brard, Laurent, Braundmeier-Fleming, Andrea
Published in PloS one (09.01.2020)

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Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study

by Garcia-Zamalloa, Alberto, Vicente, Diego, Arnay, Rafael, Arrospide, Arantzazu, Taboada, Jorge, Castilla-Rodríguez, Iván, Aguirre, Urko, Múgica, Nekane, Aldama, Ladislao, Aguinagalde, Borja, Jimenez, Montserrat, Bikuña, Edurne, Basauri, Miren Begoña, Alonso, Marta, Perez-Trallero, Emilio
Published in PloS one (04.11.2021)

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Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

by Jo, Taeho, Nho, Kwangsik, Saykin, Andrew J.
Published in Frontiers in aging neuroscience (20.08.2019)

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Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve: Result From the MACHINE Consortium

by Coenen, Adriaan, Kim, Young-Hak, Kruk, Mariusz, Tesche, Christian, De Geer, Jakob, Kurata, Akira, Lubbers, Marisa L., Daemen, Joost, Itu, Lucian, Rapaka, Saikiran, Sharma, Puneet, Schwemmer, Chris, Persson, Anders, Schoepf, U. Joseph, Kepka, Cezary, Hyun Yang, Dong, Nieman, Koen
Published in Circulation. Cardiovascular imaging (01.06.2018)

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An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology

by Waldmann, Moritz, Grosch, Alice, Witzler, Christian, Lehner, Matthias, Benda, Odo, Koch, Walter, Vogt, Klaus, Kohn, Christopher, Schröder, Wolfgang, Göbbert, Jens Henrik, Lintermann, Andreas
Published in Medical & biological engineering & computing (01.02.2022)

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Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

by Sodhi, Simrat K., Pereira, Austin, Oakley, Jonathan D., Golding, John, Trimboli, Carmelina, Russakoff, Daniel B., Choudhry, Netan
Published in PloS one (14.02.2022)

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Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer's disease

by Wang, Xin, Jiao, Bin, Liu, Hui, Wang, Yaqin, Hao, Xiaoli, Zhu, Yuan, Xu, Bei, Xu, Huizhuo, Zhang, Sizhe, Jia, Xiaoliang, Xu, Qian, Liao, Xinxin, Zhou, Yafang, Jiang, Hong, Wang, Junling, Guo, Jifeng, Yan, Xinxiang, Tang, Beisha, Zhao, Rongchang, Shen, Lu
Published in CNS neuroscience & therapeutics (01.12.2022)

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Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus—transfer learning from existing algorithms

by Grimm, Florian, Edl, Florian, Kerscher, Susanne R., Nieselt, Kay, Gugel, Isabel, Schuhmann, Martin U.
Published in Acta neurochirurgica (01.10.2020)

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Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

by Alsaih, Khaled, Lemaitre, Guillaume, Rastgoo, Mojdeh, Massich, Joan, Sidibé, Désiré, Meriaudeau, Fabrice
Published in Biomedical engineering online (07.06.2017)

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Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort

by Morin, Alexandre, Samper-Gonzalez, Jorge, Bertrand, Anne, Ströer, Sébastian, Dormont, Didier, Mendes, Aline, Coupé, Pierrick, Ahdidan, Jamila, Lévy, Marcel, Samri, Dalila, Hampel, Harald, Dubois, Bruno, Teichmann, Marc, Epelbaum, Stéphane, Colliot, Olivier
Published in Journal of Alzheimer's disease (01.01.2020)

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Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning

by Vennemann, Bernhard, Obrist, Dominik, Rösgen, Thomas
Published in PloS one (26.09.2019)

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Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography

by Hernandez, Monica, Ramon-Julvez, Ubaldo, Vilades, Elisa, Cordon, Beatriz, Mayordomo, Elvira, Garcia-Martin, Elena
Published in PloS one (07.08.2023)

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Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer’s Disease Neuroimaging (ADNI) database

by Beltrán, Juan Felipe, Wahba, Brandon Malik, Hose, Nicole, Shasha, Dennis, Kline, Richard P.
Published in PloS one (27.07.2020)

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Prediction and classification of Alzheimer disease based on quantification of MRI deformation

by Long, Xiaojing, Chen, Lifang, Jiang, Chunxiang, Zhang, Lijuan
Published in PloS one (06.03.2017)

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Automatically measuring brain ventricular volume within PACS using artificial intelligence

by Yepes-Calderon, Fernando, Nelson, Marvin D., McComb, J. Gordon
Published in PloS one (15.03.2018)

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Validation of automated artificial intelligence segmentation of optical coherence tomography images

by Maloca, Peter M., Lee, Aaron Y., de Carvalho, Emanuel R., Okada, Mali, Fasler, Katrin, Leung, Irene, Hörmann, Beat, Kaiser, Pascal, Suter, Susanne, Hasler, Pascal W., Zarranz-Ventura, Javier, Egan, Catherine, Heeren, Tjebo F. C., Balaskas, Konstantinos, Tufail, Adnan, Scholl, Hendrik P. N.
Published in PloS one (16.08.2019)

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An investigation of machine learning methods in delta-radiomics feature analysis

by Chang, Yushi, Lafata, Kyle, Sun, Wenzheng, Wang, Chunhao, Chang, Zheng, Kirkpatrick, John P., Yin, Fang-Fang
Published in PloS one (13.12.2019)

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An interpretable machine learning model for diagnosis of Alzheimer's disease

by Das, Diptesh, Ito, Junichi, Kadowaki, Tadashi, Tsuda, Koji
Published in PeerJ (San Francisco, CA) (01.03.2019)

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Machine Learning Techniques for Ophthalmic Data Processing: A Review

by Sarhan, Mhd Hasan, Nasseri, M. Ali, Zapp, Daniel, Maier, Mathias, Lohmann, Chris P., Navab, Nassir, Eslami, Abouzar
Published in IEEE journal of biomedical and health informatics (01.12.2020)

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Intracranial pressure based decision making: Prediction of suspected increased intracranial pressure with machine learning

by Miyagawa, Tadashi, Sasaki, Minami, Yamaura, Akira
Published in PloS one (21.10.2020)

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