首页|Data on Machine Learning Discussed by a Researcher at Nagasaki University Graduate School of Biomedical Sciences (Machine- Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy)
Data on Machine Learning Discussed by a Researcher at Nagasaki University Graduate School of Biomedical Sciences (Machine- Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy)
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Current study results on artificial intelligence have been published. According to news reporting from Nagasaki, Japan, by NewsRx journalists, research stated, "When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge." Financial supporters for this research include New Energy And Industrial Technology Development Organization. The news journalists obtained a quote from the research from Nagasaki University Graduate School of Biomedical Sciences: "Our objective is to develop a machine-learning-based classifier for TBLB specimens. Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP), as potential histologic markers to distinguish between benign and malignant conditions. A total of 251 TBLB cases with defined benign and malignant outcomes based on clinical follow-up were collected and a gradient-boosted decision-tree-based machine learning model (XGBoost) was trained and tested on randomly split training and test sets. Five pathological changes showed independent, mild-to-moderate associations (AUC ranging from 0.58 to 0.75) with benign conditions, with IB/B being the strongest predictor. On the other hand, FE emerged to be the sole indicator of malignant conditions with a mild association (AUC = 0.66). Our model was trained on 200 cases and tested on 51 cases, achieving an AUC of 0.78 for the binary classification of benign vs. malignant on the test set."
Nagasaki University Graduate School of Biomedical SciencesNagasakiJapanAsiaCyborgsEmerging TechnologiesMachine Learning
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