Jani Saunavaara

Jatin Chaudhary, Ivan Jambor, Hannu Aronen et al.

Background: The reproducibility of machine-learning models in prostate cancer detection across different MRI vendors remains a significant challenge. Methods: This study investigates Support Vector Machines (SVM) and Random Forest (RF) models trained on radiomic features extracted from T2-weighted MRI images using Pyradiomics and MRCradiomics libraries. Feature selection was performed using the maximum relevance minimum redundancy (MRMR) technique. We aimed to enhance clinical decision support through multimodal learning and feature fusion. Results: Our SVM model, utilizing combined features from Pyradiomics and MRCradiomics, achieved an AUC of 0.74 on the Multi-Improd dataset (Siemens scanner) but decreased to 0.60 on the Philips test set. The RF model showed similar trends, with notable robustness for models using Pyradiomics features alone (AUC of 0.78 on Philips). Conclusions: These findings demonstrate the potential of multimodal feature integration to improve the robustness and generalizability of machine-learning models for clinical decision support in prostate cancer detection. This study marks a significant step towards developing reliable AI-driven diagnostic tools that maintain efficacy across various imaging platforms.

Daniel Aalto, Antti Huhtala, Atle Kivelä et al.

We compare numerically computed resonances of the human vocal tract with formants that have been extracted from speech during vowel pronunciation. The geometry of the vocal tract has been obtained by MRI from a male subject, and the corresponding speech has been recorded simultaneously. The resonances are computed by solving the Helmholtz partial differential equation with the Finite Element Method (FEM). Despite a rudimentary exterior space acoustics model, i.e., the Dirichlet boundary condition at the mouth opening, the computed resonance structure differs from the measured formant structure by $\approx$ 0.7 semitones for [i] and [u] having small mouth opening area, and by $\approx$ 3 semitones for vowels [a] and [ae] that have a larger mouth opening. The contribution of the possibly open velar port has not been taken into considaration at all which adds the discrepancy for [a] in the present data set. We conclude that by improving the exterior space model and properly treating the velar port opening, it is possible to computationally attain four lowest vowel formants with an error less than a semitone. The corresponding wave equation model on MRI-produced vocal tract geometries is expected to have a comparable accuracy.