Kurstjens, Steef de Bel, Thomas van der Horst, Armando Kusters, Ron Krabbe, Johannes van Balveren, Jasmijn
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
Objectives Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anem...
Soerensen, Patricia Diana Christensen, Henry Gray Worsoe Laursen, Soeren Hardahl, Christian Brandslund, Ivan Madsen, Jonna Skov
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
Objectives To evaluate the ability of an artificial intelligence (AI) model to predict the risk of cancer in patients referred from primary care based on routine blood tests. Results obtained with the AI model are compared to results based on logistic regression (LR). Methods An analytical profile consisting of 25 predefined routine laboratory bloo...
Pennestrì, Federico Banfi, Giuseppe
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by ...
Pei, Qin Luo, Yanan Chen, Yiyu Li, Jingyuan Xie, Dan Ye, Ting
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
Artificial intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality...
Hatami, Behzad Asadi, Farkhondeh Bayani, Azadeh Zali, Mohammad Reza Kavousi, Kaveh
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
Objectives The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis. Methods In the present study, we used modern machine learning (ML) methods to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degree...
Farrell, Christopher-John L.
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
Objectives Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural ne...
Bayani, Azadeh Asadi, Farkhondeh Hosseini, Azamossadat Hatami, Behzad Kavousi, Kaveh Aria, Mehrad Zali, Mohammad Reza
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
Objectives All patients with cirrhosis should be periodically examined for esophageal varices (EV), however, a large percentage of patients undergoing screening, do not have EV or have only mild EV and do not have high-risk characteristics. Therefore, developing a non-invasive method to predict the occurrence of EV in patients with liver cirrhosis ...
Zhou, Rui Liang, Yu-fang Cheng, Hua-Li Wang, Wei Huang, Da-wei Wang, Zhe Feng, Xiang Han, Ze-wen Song, Biao Padoan, Andrea
...
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
Objectives Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed...
Carobene, Anna Milella, Frida Famiglini, Lorenzo Cabitza, Federico
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature ...
Negrini, Davide Danese, Elisa Henry, Brandon M. Lippi, Giuseppe Montagnana, Martina
Published in
Clinical Chemistry and Laboratory Medicine (CCLM)
Objectives The development and use of artificial intelligence (AI) methodologies, especially machine learning (ML) and deep learning (DL), have been considerably fostered during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Several models and algorithms have been developed and applied for both identifying COVID-19 cases and for assessin...
