首页|Bambino Gesu Children's Hospital Reports Findings in Machine Learning (Fit of biokinetic data in molecular radiotherapy: a machine learning approach)
Bambino Gesu Children's Hospital Reports Findings in Machine Learning (Fit of biokinetic data in molecular radiotherapy: a machine learning approach)
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New research on Machine Learning is the subject of a report. According to news reporting out of Rome, Italy, by NewsRx editors, research stated, “In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks.” Our news journalists obtained a quote from the research from Bambino Gesu Children’s Hospital, “The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (t). Two different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [I]I-NaI. Test performances, defined as classification accuracy (CA) and percentage difference between the actual and the estimated area under the curve (Dt), were compared with those obtained using AM varying the number of points (N) of the TACs. A comparison between AM and ML were performed using data of 20 real patients. As N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, [Formula: see text] can reach down to - 67%, while using ML [Formula: see text] ranges within ± 25%. Using real TACs, there is a good agreement between t obtained with ML system and AM.”
RomeItalyEuropeCyborgsDrugs and TherapiesEmerging TechnologiesHealth and MedicineMachine LearningRadiotherapy
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