Results 171 to 180 of about 267,347 (270)

Shapley Additive Explanation for Local Class Differentiation: Local Explainability for Class Differentiation in Classification Models

Advanced Intelligent Systems, EarlyView.
An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna, Hugo A. Ordoñez Erazo, Carlos A. Cobos Lozada   +2 more
wiley   +1 more source

Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges. [PDF]

Front Radiol
Zhao H, Xue T, Zhang Y, Lu Y, Wang D, Yin P, Jiang L, Liu D, Wang Y.   +8 more
europepmc   +1 more source

AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

AI &Innovation, EarlyView.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury, Kandarpa Kumar Sarma, Lachit Dutta   +2 more
wiley   +1 more source

Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients. [PDF]

Nurs Crit Care
Guven S, Aslan FE, Canayaz M.
europepmc   +1 more source

Developmental and Phenotypic Outcomes in Mild Phenylalanine Hydroxylase Deficiency

American Journal of Medical Genetics Part A, EarlyView.
ABSTRACT Benign hyperphenylalaninemia (bHPA) is defined as elevated phenylalanine (Phe) levels remaining ≤ 360 μmol/L (6 mg/dL) and not requiring medical intervention. Individuals with bHPA may demonstrate a rise in their Phe levels > 360 μmol/L, effectively developing a mild PKU phenotype requiring therapy to prevent neurocognitive complications. This
Aaron Williams, Kristian Divin, Lindsay C. Burrage, William J. Craigen, Fernando Scaglia, Claudia Soler‐Alfonso, V. Reid Sutton, Kevin E. Glinton, Ronit Marom   +8 more
wiley   +1 more source

Application of machine learning and deep learning in the diagnosis and treatment of inguinal hernia: a narrative review. [PDF]

Front Med (Lausanne)
Liu Y, Xie TH, Fu Y, Ha SN, Jin XS, Ren XX.   +5 more
europepmc   +1 more source

Unraveling a Diagnostic Enigma: A TECPR2 Case Solved Through Multi‐Omic Genomics

American Journal of Medical Genetics Part A, EarlyView.
ABSTRACT TECPR2 is a key regulator of autophagy, encoded by the TECPR2 gene. Pathogenic variants in this gene have been linked to a rare hereditary sensory and autonomic neuropathy with intellectual disability (HSAN9). We report a teenage female with a syndromic intellectual disability disorder associated with neuromuscular abnormalities.
Teresa Zhao, Andrew P. Fennell, Tanavi Sharma, Katrina M. Bell, Monique Dunstan, Sebastian Lunke, Meagan J. McGrath, Catriona McLean, Undiagnosed Diseases Network (UDN‐Aus), Alison Yeung, Anna Hackett, Anne Baxter, Ansley Morrish, Ashil Davawala, Azure Hermes, Ben Kamien, Ben Lundie, Carolyn Ellaway, Carolyn Shalhoub, Cas Simons, Cassandra Gray, Cathryn Poulton, Chloe Cunningham, Chris Barnett, Chris Richmond, Christopher Richards, Daniel MacArthur, Daniel Pavlic, Daniella Hock, Daniz Kooshavar, David Amor, David Mowat, Edward Formaini, Elaine Zhang, Ella Wilkins, Ella Zurita, Ellenore Martin, Elly Lynch, Emma Krzesinski, Emma Palmer, Esther Pierini, Evanthia O. Madelli, Francisco Santos Gonzalez, Gareth Baynam, Gunjan Garg, Hamish Scott, Hannah Thomson, Himanshu Goel, Ilias Goranitis, Ira Deveson, Isabella Pfundt, Jacqui Russell, Janine Smith, Jason Pinner, Julia Broadbent, Julie McGaughran, Karin Kassahn, Katherine Lewis, Kaustuv Bhattacharya, Kirsten Boggs, Kirsty West, Kristi Jones, Laura Wedd, Lauren Dreyer, Leah Frajman, Leanne Baxter, Lilian Downie, Lily Loughman, Lisa Bristowe, Lisa Ewans, Louise Cilento, Lucy Kevin, Lyndal Douglas, Madeleine Harris, Maie Walsh, Manisha Chauhan, Margit Shah, Martin Delatycki, Mathew Wallis, Matthew Hunter, Megan Ball, Megan Higgins, Meutia Kumaheri, Michael Fahey, Mike Field, Mohammadreza Hajjari, Natalie Stewart, Natalie Tan, Natasha Brown, Nicole Van Bergen, Noelia Nunez‐Martinez, Oliver Heath, Rachel Austin, Rani Sachdev, Rebecca Macintosh, Rebecca Vink, Rocio Rius, Ruvi Samarasekera, Ryan Pysar, Sandra Cooper, Sarah Casauria, Sarah Collinson, Sarah Jelenich, Sarah Josephi‐Taylor, Sarah Sandaradura, Sean Massey, Shannon Leblanc, Shuxiang Goh, Simon Bodek, Simon Sadedin, Simran Kaur, Smitha Kumble, Sue White, Suzanne Sallevelt, Tegan Stait, Tiffany Boughtwood, Tim Sikora, Timo Lassmann, Tiong Tan, Tracy Dudding, David R. Thorburn, David A. Stroud, John Christodoulou   +122 more
wiley   +1 more source

Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review. [PDF]

Quant Imaging Med Surg
Ming W, Liu T, Hu R, He W, Yang Y.
europepmc   +1 more source

Mapping quantitative trait loci underlying body weight changes that act at different times during high‐fat diet challenge in collaborative cross mice

Animal Models and Experimental Medicine, EarlyView.
Over one billion people worldwide suffer from obesity, and the number is continually rising. Animal models, especially mouse models, are crucial to identifying the genetic components of complex disorders and exploring the potential applications of these genetic findings.
Hanifa J. Abu‐Toamih Atamni, Iqbal M. Lone, Ilona Binenbaum, Kareem Midlej, Eleftherios Pilalis, Richard Mott, Aristotelis Chatziioannou, Fuad A. Iraqi   +7 more
wiley   +1 more source

Deep Learning-based Automated Coronary Plaque Quantification: First Demonstration With Ultra-high Resolution Photon-counting Detector CT at Different Temporal Resolutions. [PDF]

Invest Radiol
Klambauer K, Burger SD, Demmert TT, Mergen V, Moser LJ, Gulsun MA, Schöbinger M, Schwemmer C, Wels M, Allmendinger T, Eberhard M, Alkadhi H, Schmidt B.   +12 more
europepmc   +1 more source