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Theocharopoulos C, Theocharopoulos A, Papadakos SP, Machairas N, Pawlik TM. Deep Learning to Enhance Diagnosis and Management of Intrahepatic Cholangiocarcinoma. Cancers (Basel) 2025; 17:1604. [PMID: 40427103 PMCID: PMC12110721 DOI: 10.3390/cancers17101604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2025] [Revised: 05/03/2025] [Accepted: 05/07/2025] [Indexed: 05/19/2025] Open
Abstract
Intrahepatic cholangiocarcinoma (iCCA) is associated with a poor prognosis and necessitates a multimodal, multidisciplinary approach from diagnosis to treatment to achieve optimal outcomes. A noninvasive preoperative diagnosis using abdominal imaging techniques can represent a clinical challenge. Given the differential response of iCCA to localized and systemic therapies compared with hepatocellular carcinoma and secondary hepatic malignancies, an accurate diagnosis is crucial. Deep learning (DL) models for image analysis have emerged as a promising adjunct for the abdominal radiologist, potentially enhancing the accurate detection and diagnosis of iCCA. Over the last five years, several reports have proposed robust DL models, which demonstrate a diagnostic accuracy that is either comparable to or surpasses that of radiologists with varying levels of experience. Recent studies have expanded DL applications into other aspects of iCCA management, including histopathologic diagnosis, the prediction of histopathological features, the preoperative prediction of survival, and the pretreatment prediction of responses to systemic therapy. We herein critically evaluate the expanding body of research on DL applications in the diagnosis and management of iCCA, providing insights into the current progress and future research directions. We comprehensively synthesize the performance and limitations of DL models in iCCA research, identifying key challenges that serve as a translational reference for clinicians.
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Affiliation(s)
- Charalampos Theocharopoulos
- Second Department of Propaedeutic Surgery, Laiko General Hospital, School of Medicine, National Kapodistrian University of Athens, 11527 Athens, Greece
| | - Achilleas Theocharopoulos
- Department of Electrical and Computer Engineering, National Technical University of Athens, 10682 Athens, Greece
| | - Stavros P. Papadakos
- Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Nikolaos Machairas
- Second Department of Propaedeutic Surgery, Laiko General Hospital, School of Medicine, National Kapodistrian University of Athens, 11527 Athens, Greece
| | - Timothy M. Pawlik
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH 43210, USA
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Mateo F, Mateo EM, Tarazona A, García-Esparza MÁ, Soria JM, Jiménez M. New Strategies and Artificial Intelligence Methods for the Mitigation of Toxigenic Fungi and Mycotoxins in Foods. Toxins (Basel) 2025; 17:231. [PMID: 40423314 DOI: 10.3390/toxins17050231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2025] [Revised: 05/03/2025] [Accepted: 05/05/2025] [Indexed: 05/28/2025] Open
Abstract
The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute a significant concern in the fields of public health and consumer protection. This review highlights recent strategies and emerging methods aimed at preventing fungal growth and mycotoxin contamination in food matrices as opposed to traditional approaches such as chemical fungicides, which may leave toxic residues and pose risks to human and animal health as well as the environment. The novel methodologies discussed include the use of plant-derived compounds such as essential oils, classified as Generally Recognized as Safe (GRAS), polyphenols, lactic acid bacteria, cold plasma technologies, nanoparticles (particularly metal nanoparticles such as silver or zinc nanoparticles), magnetic materials, and ionizing radiation. Among these, essential oils, polyphenols, and lactic acid bacteria offer eco-friendly and non-toxic alternatives to conventional fungicides while demonstrating strong antimicrobial and antifungal properties; essential oils and polyphenols also possess antioxidant activity. Cold plasma and ionizing radiation enable rapid, non-thermal, and chemical-free decontamination processes. Nanoparticles and magnetic materials contribute advantages such as enhanced stability, controlled release, and ease of separation. Furthermore, this review explores recent advancements in the application of artificial intelligence, particularly machine learning methods, for the identification and classification of fungal species as well as for predicting the growth of toxigenic fungi and subsequent mycotoxin production in food products and culture media.
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Affiliation(s)
- Fernando Mateo
- Department of Electronic Engineering, ETSE, (UV), Burjassot, 46100 Valencia, Spain
| | - Eva María Mateo
- Department of Microbiology and Ecology, Faculty of Medicine and Odontology, University of Valencia (UV), 46010 Valencia, Spain
| | - Andrea Tarazona
- Department of Microbiology and Ecology, Faculty of Biology, (UV), Burjassot, 46100 Valencia, Spain
| | | | - José Miguel Soria
- Department of Biomedical Sciences, Cardenal Herrera University-CEU Universities, 46115 Valencia, Spain
| | - Misericordia Jiménez
- Department of Microbiology and Ecology, Faculty of Biology, (UV), Burjassot, 46100 Valencia, Spain
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Ozmen BB, Phuyal D, Berber I, Schwarz GS. Artificial intelligence prediction model for readmission after DIEP flap breast reconstruction based on NSQIP data. J Plast Reconstr Aesthet Surg 2025; 106:1-8. [PMID: 40347912 DOI: 10.1016/j.bjps.2025.04.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Revised: 03/25/2025] [Accepted: 04/16/2025] [Indexed: 05/14/2025]
Abstract
BACKGROUND Readmissions following deep inferior epigastric perforator (DIEP) flap breast reconstruction represent a significant healthcare burden, yet current risk prediction methods lack precision in identifying high-risk patients. We developed a machine learning model to predict 30-day readmission risk using a large national surgical quality database. METHODS This retrospective analysis examined 13,312 DIEP flap procedures from the American College of Surgeons National Surgical Quality Improvement Program database (2016-2022). A stacked machine learning model was developed incorporating patient demographics, comorbidities, operative characteristics, and laboratory values. Model performance was assessed using accuracy, precision, recall, and F1 score. RESULTS The overall readmission rate was 5.42% (723 patients). The stacked model achieved 88% accuracy and 79% recall for readmission prediction with an area under the receiver operating characteristic curve of 0.8921 (95% CI: 0.853-0.927) on the test set. Key predictors included days from operation until superficial incisional surgical site infection complications, operative time, body mass index, and preoperative albumin. CONCLUSION This stacked machine learning approach demonstrates strong predictive capability for post-DIEP flap readmissions, with high sensitivity for identifying at-risk patients. The model's performance suggests clinical utility in preoperative risk stratification and resource allocation. Implementation could enable targeted intervention strategies to potentially reduce readmission rates in high-risk populations.
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Affiliation(s)
- Berk B Ozmen
- Department of Plastic Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Diwakar Phuyal
- Department of Plastic Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Ibrahim Berber
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Graham S Schwarz
- Department of Plastic Surgery, Cleveland Clinic, Cleveland, OH, USA.
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Araújo R, Ramalhete L, Von Rekowski CP, Fonseca TAH, Calado CRC, Bento L. Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients. Metabolites 2025; 15:204. [PMID: 40137168 PMCID: PMC11944015 DOI: 10.3390/metabo15030204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2025] [Revised: 03/03/2025] [Accepted: 03/14/2025] [Indexed: 03/27/2025] Open
Abstract
Background: Timely and accurate identification of bloodstream infections (BSIs) in intensive care unit (ICU) patients remains a key challenge, particularly in COVID-19 settings, where immune dysregulation can obscure early clinical signs. Methods: Cytokine profiling was evaluated to discriminate between ICU patients with and without BSIs, and, among those with confirmed BSIs, to further stratify bacterial infections by Gram type. Serum samples from 45 ICU COVID-19 patients were analyzed using a 21-cytokine panel, with feature selection applied to identify candidate markers. Results: A machine learning workflow identified key features, achieving robust performance metrics with AUC values up to 0.97 for BSI classification and 0.98 for Gram typing. Conclusions: In contrast to traditional approaches that focus on individual cytokines or simple ratios, the present analysis employed programmatically generated ratios between pro-inflammatory and anti-inflammatory cytokines, refined through feature selection. Although further validation in larger and more diverse cohorts is warranted, these findings underscore the potential of advanced cytokine-based diagnostics to enhance precision medicine in infection management.
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Affiliation(s)
- Rúben Araújo
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal; (R.A.)
- CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
- ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
| | - Luís Ramalhete
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal; (R.A.)
- IPST—Instituto Português do Sangue e da Transplantação, Alameda das Linhas de Torres 117, 1769-001 Lisbon, Portugal
- iNOVA4Health—Advancing Precision Medicine, RG11: Reno-Vascular Diseases Group, NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
| | - Cristiana P. Von Rekowski
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal; (R.A.)
- CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
- ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
| | - Tiago A. H. Fonseca
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal; (R.A.)
- CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
- ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
| | - Cecília R. C. Calado
- ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
- Institute for Bioengineering and Biosciences (iBB), The Associate Laboratory Institute for Health and Bioeconomy-i4HB, Instituto Superior Técnico (IST), Universidade de Lisboa (UL), Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Luís Bento
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal; (R.A.)
- CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
- Intensive Care Department, ULSSJ—Unidade Local de Saúde São José, Rua José António Serrano, 1150-199 Lisbon, Portugal
- Integrated Pathophysiological Mechanisms, CHRC—Comprehensive Health Research Centre, NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal
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Dillner RP, Wimmer MA, Porten M, Udelhoven T, Retzlaff R. Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines. SENSORS (BASEL, SWITZERLAND) 2025; 25:431. [PMID: 39860800 PMCID: PMC11769238 DOI: 10.3390/s25020431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Revised: 12/29/2024] [Accepted: 12/31/2024] [Indexed: 01/27/2025]
Abstract
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features). The specific features were selected based on extensive literature research, including especially the fields of precision agri- and viticulture. To integrate only vine canopy-exclusive features into ML classifications, different feature types were extracted and spatially aggregated (zonal statistics), based on a combined pixel- and object-based image-segmentation-technique-created vine row mask around each single grapevine position. The extracted canopy features were progressively grouped into seven input feature groups for model training. Model overall performance metrics were optimized with grid search-based hyperparameter tuning and repeated-k-fold-cross-validation. Finally, ML-based growth class prediction results were extensively discussed and evaluated for overall (accuracy, f1-weighted) and growth class specific- classification metrics (accuracy, user- and producer accuracy).
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Affiliation(s)
- Ronald P. Dillner
- Department of Viticulture and Oenology, DLR (Dienstleistungszentrum Ländlicher Raum) Mosel/Steillagenzentrum, Gartenstraße 18, 54470 Bernkastel-Kues, Germany;
| | - Maria A. Wimmer
- Department of Computer Science, University of Koblenz, Universitätsstraße 1, 56070 Koblenz, Germany;
| | - Matthias Porten
- Department of Viticulture and Oenology, DLR (Dienstleistungszentrum Ländlicher Raum) Mosel/Steillagenzentrum, Gartenstraße 18, 54470 Bernkastel-Kues, Germany;
| | - Thomas Udelhoven
- Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany; (T.U.); (R.R.)
| | - Rebecca Retzlaff
- Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany; (T.U.); (R.R.)
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