Parkinson’s disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson’s disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.

Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3′54″ to 85′54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2′03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines.

Background: The current high standards in orthognathic surgery demand surgical solutions that are both ⁠ functionally ⁠ effective and aesthetically pleasing. Our approach offers one for enhanced stability, attractiveness, and nerve protection ⁠ with improved accessibility ⁠ in the majority of orthognathic scenarios ⁠ compared to an inverted L osteotomy. Methods: A case series is presented to illustrate the application and outcomes of HSSO, an optimised approach that combines the advantages of a transoral inverted L osteotomy with specific enhancements and increased versatility, ⁠ with accessibility and exposure similar to a BSSO. Results: HSSO as a completely transoral technique, demonstrate the ability to perform significant counterclockwise rotations of the mandible, eliminating the need for trocars or skin incisions. We experinced high postoperative stability when HSSO was performed in conjunction with a three-piece LeFort 1 osteotomy on a dynamic opposing arch. In comparison to an inverted L approach, we postulated that HSSO offers advantages in stability, due to the increased segmental overlap of the proximal and distal segments of the mandible. This approach is designed to enhance the safety of the inferior alveolar nerve compared to traditional sagittal split methods. Furthermore, HSSO represents an alternative to total joint replacement in select cases of idiopathic condylar resorption and is effective for correcting mandibular asymmetries while maintaining jawline aesthetics. This is achieved through the manipulation of the mandibular angle, ramus height, and inferior border without creating a step deformity in the soft tissue. Conclusions: The outcomes of HSSO highlight its capacity to deliver predictable, functional, and aesthetically pleasing results, offering a viable alternative to more traditional orthognathic techniques.

In maxillofacial surgery, orbital reconstruction requires precision to address both functional and aesthetic considerations arising from both acute and elective conditions. This study presents a novel, fully automated segmentation software designed specifically for the orbital floor. This software enhances surgical planning through superior accuracy, efficiency, and usability. The authors' transdisciplinary team compiled a dataset of 1004 expert-segmented orbits from computed tomography images across multiple countries, ensuring broad anatomical representation. Developed with the nnU-Net framework, the software achieved segmentation accuracy with a mean Dice similarity coefficient of 0.935 and a mean surface distance of 0.292 mm for the orbit, and a Dice similarity coefficient of 0.917 and mean surface distance of 0.287 mm for the orbital floor-all within approximately 1 s. This performance surpasses traditional manual segmentation, which averages 25 min per orbit. The system delivered consistent results across a range of imaging sources, affirming its reliability for a wide range of clinical applications. By introducing this fully automated, high-precision tool, this study pioneers advancements in AI-driven orbital reconstructions, setting new standards for patient-specific surgical planning. Further development and integration holds the key to transforming the field, ensuring ethical compliance and enhancing informed consent.

Orbital reconstruction after trauma or pathology requires precise restoration of anatomical symmetry to prevent functional and aesthetic impairment. Although virtual surgical planning and computer-aided design/manufacturing have significantly improved workflows, segmentation and mirroring of the orbit remain primarily manual and operator-dependent. This study developed and validated a fully automated, AI-based workflow that integrates deep learning-driven segmentation and algorithmic mirroring for improved efficiency and reproducibility in orbital reconstruction. A multicenter dataset comprising 502 cranial CT scans from Germany, Romania, and the USA was employed to train and evaluate a full-resolution 3D nnU-Net model for automated orbital segmentation. Following segmentation, a mirroring algorithm using Principal Component Analysis and Iterative Closest Point registration computed the sagittal symmetry plane. Automated outcomes were benchmarked against manual segmentation and mirroring by clinical experts, using metrics including Dice Similarity Coefficient, Mean Surface Distance, Hausdorff Distance, and angular deviation between symmetry planes. The AI-driven segmentation achieved high accuracy (mean DSC 0.936), with superior performance in non-fractured orbits (DSC up to 0.941). Angular deviation from manually defined symmetry planes was minimal (fractured: 0.95° ± 0.65°; non-fractured: 0.72° ± 0.53°), consistent across institutions. This automated approach demonstrated robust, clinically applicable segmentation and mirroring accuracy, enabling standardized and efficient orbital reconstruction workflows.