Three-dimensional (3D) reconstruction of lower limbs is essential for surgical planning and clinical outcome evaluation. 3D reconstruction from biplanar calibrated radiographs may be an alternative to irradiation issues of CT-scan. A previous study proposed a two-step reconstruction method based on parametric models and statistical inferences leading to a fast Initial Solution (IS) followed by manual adjustments. This study aims to improve the IS using a new 3D database, a novel parametric model of the tibia and a different regression approach. The IS was evaluated in terms of shape accuracy on 9 lower limbs and reproducibility of clinical measurements on 22 lower limbs. Reconstruction time was also evaluated. Comparison to the previous method showed an improvement of the IS in terms of shape accuracy (1.3 vs. 1.6 and 2 mm respectively for both femur and tibia) and reproducibility of clinical measurements (i.e. 3.1° vs. 8.3° for neck-shaft-angle; 4.2° and 5° vs. 5° and 6° for tibial and femoral torsion respectively). The proposed approach constitutes a considerable step towards an automatic 3D reconstruction of lower limb.

An original three-dimensional (3D) linear articulatory model of the velum and nasopharyngeal wall has been developed from magnetic resonance imaging (MRI) and computed tomography images of a French subject sustaining a set of 46 articulations, covering his articulatory repertoire. The velum and nasopharyngeal wall are represented by generic surface triangular meshes fitted to the 3D contours extracted from MRI for each articulation. Two degrees of freedom were uncovered by principal component analysis: first, VL accounts for 83% of the velum variance, corresponding to an oblique vertical movement seemingly related to the levator veli palatini muscle; second, VS explains another 6% of the velum variance, controlling a mostly horizontal movement possibly related to the sphincter action of the superior pharyngeal constrictor. The nasopharyngeal wall is also controlled by VL for 47% of its variance. Electromagnetic articulographic data recorded on the velum fitted these parameters exactly, and may serve to recover dynamic velum 3D shapes. The main oral and nasopharyngeal area functions controlled by the articulatory model, complemented by the area functions derived from the complex geometry of each nasal passage extracted from coronal MRIs, were fed to an acoustic model and gave promising results about the influence of velum movements on the spectral characteristics of nasals.

Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.

Scientists seek to use fossil and archaeological evidence to constrain models of the coevolution of human language and tool use. We focus on Neanderthals, for whom indirect evidence from tool use and ancient DNA appears consistent with an adaptation to complex vocal-auditory communication. We summarize existing arguments that the articulatory apparatus for speech had not yet come under intense positive selection pressure in Neanderthals, and we outline some recent evidence and analyses that challenge such arguments. We then provide new anatomical results from our own attempt to reconstruct vocal tract (VT) morphology in Neanderthals, and document our simulations of the acoustic and articulatory potential of this reconstructed Neanderthal VT. Our purpose in this paper is not to polarize debate about whether or not Neanderthals were human-like in all relevant respects, but to contribute to the development of methods that can be used to make further incremental advances in our understanding of the evolution of speech based on fossil and archaeological evidence.

This paper analyzes the influence of pregnancy stage and fetus position on the whole-body and brain exposure of the fetus to radiofrequency electromagnetic fields. Our analysis is performed using semi-homogeneous pregnant woman models between 8 and 32 weeks of amenorrhea. By analyzing the influence of the pregnancy stage on the environmental whole-body and local exposure of a fetus in vertical position, head down or head up, in the 2100 MHz frequency band, we concluded that both whole-body and average brain exposures of the fetus decrease during the first pregnancy trimester, while they advance during the pregnancy due to the rapid weight gain of the fetus in these first stages. From the beginning of the second trimester, the whole-body and the average brain exposures are quite stable because the weight gains are quasi proportional to the absorbed power increases. The behavior of the fetus whole-body and local exposures during pregnancy for a fetus in the vertical position with the head up were found to be of a similar level, when compared to the position with the head down they were slightly higher, especially in the brain.

The various speech sounds of a language are obtained by varying the shape and position of the articulators surrounding the vocal tract. Analyzing their variations is crucial for understanding speech production, diagnosing speech disorders and planning therapy. Identifying key anatomical landmarks of these structures on medical images is a pre-requisite for any quantitative analysis and the rising amount of data generated in the field calls for an automatic solution. The challenge lies in the high inter- and intra-speaker variability, the mutual interaction between the articulators and the moderate quality of the images. This study addresses this issue for the first time and tackles it by means of Deep Learning. It proposes a dedicated network architecture named Flat-net and its performance are evaluated and compared with eleven state-of-the-art methods from the literature. The dataset contains midsagittal anatomical Magnetic Resonance Images for 9 speakers sustaining 62 articulations with 21 annotated anatomical landmarks per image. Results show that the Flat-net approach outperforms the former methods, leading to an overall Root Mean Square Error of 3.6 pixels/0.36 cm obtained in a leave-one-out procedure over the speakers. The implementation codes are also shared publicly on GitHub.