Die erste Publikation geht auf das Jahr 2020 zurück.
2023
M. Scholz, S. Lakämper, K. Keller, A. Dobay, A. Steuer, H.-P. Landolt, T. Kraemer.
Metabolomics-based Sleepiness Markers for Risk Prevention and Traffic Safety (ME-SMART): a monocentric, controlled, randomized, crossover trial
Trials 24 131 (2023).
V. Ibanez, D. Jucker, L. C. Ebert, S. Franckenberg, A. Dobay.
Classification of rib fracture types from postmortem computed tomography images using deep learning
Forensic Sci Med Pathol (2023).
N. Zimmermann, T. Sieberth, A. Dobay.
Automated wound segmentation and classification of seven common injuries in forensic medicine
Forensic Sci Med Pathol (2023).
M. Lory, M. Bovens, A. Dobay.
Der Einsatz von „Künstlicher Intelligenz" in der forensischen Fallarbeit. Teil 1: Wie bekommen wir Maschinen in den Griff?
Kriminalistik - Schweiz 77(3) (2023), 178–183.
M. Lory, M. Bovens.
Der Einsatz von „Künstlicher Intelligenz" in der forensischen Fallarbeit. Teil 2: Die Maschine findet noch geringste Reste von Treibstoffbenzin im Brandschutt
Kriminalistik - Schweiz 77(7) (2023), 426–432.
2022
G. Streun, A. Steuer, S. Pötzsch, L. C. Ebert, A. Dobay, T. Kraemer.
Towards a qualitative screening assay for synthetic cannabinoids in urine using a high-resolution mass spectrometry untargeted metabolomics approach together with a random forest classifier.
Clinical Chemistry 68 (2022), 848–855.
C. Bogdal, R. Schellenberg, M. Lory, M. Bovens, O. Höpli.
Recognition of gasoline in fire debris using machine learning: Part II, application of a neural network.
Forensic Science International 332, (2022), 111177.
C. Bogdal, R. Schellenberg, O. Höpli, M. Bovens, M. Lory.
Recognition of gasoline in fire debris using machine learning: Part I, application of random forest, gradient boosting, support vector machine, and naïve bayes.
Forensic Science International 331, (2022), 111146.
V. Ibañez, S. Gunz, S. Erne, J. E. Rawdon, G. Ampanozi, S. Franckenberg, T. Sieberth, R. Affolter, L. C. Ebert and A. Dobay.
RiFNet: Automated rib fracture detection in postmortem computed tomography image.
Forensic Sci Med Pathol 18 (2022), 20-29.
2021
R. Golomingi, C. Haas, A. Dobay, S. Kottner, L. C. Ebert.
Sperm hunting on optical microscope slides for forensic analysis with deep convolutional networks - a feasibility study.
Forensic Sci Int Genet 56 (2021), 102602.
G. L. Streun, A. E. Steuer, L. C. Ebert, A. Dobay and T. Kraemer.
Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry.
Clinical Chemistry and Laboratory Medicine 59 (2021), 1392-1399.
2020
A. Dobay, J. Ford, S. Decker, G. Ampanozi, S. Franckenberg, T. Sieberth, R. Affolter, and L. C. Ebert.
Potential use of deep learning techniques for postmortem imaging.
Forensic Sci Med Pathol 16 (2020), 671–679.
G. L. Streun, M. P. Elmiger, A. Dobay, L. C. Ebert, T. Kraemer.
A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules – Proof of concept study using an artificial neural network for sample classification.
Drug Test Anal. 12 (2020), 836–845.