Surgical planning tool for robotically assisted hearing aid implantation

PURPOSE : For the facilitation of minimally invasive robotically performed direct cochlea access (DCA) procedure, a surgical planning tool which enables the surgeon to define landmarks for patient-to-image registration, identify the necessary anatomical structures and define a safe DCA trajectory using patient image data (typically computed tomography (CT) or cone beam CT) is required. To this end, a dedicated end-to-end software planning system for the planning of DCA procedures that addresses current deficiencies has been developed. METHODS :    Efficient and robust anatomical segmentation is achieved through the implementation of semiautomatic algorithms; high-accuracy patient-to-image registration is achieved via an automated model-based fiducial detection algorithm and functionality for the interactive definition of a safe drilling trajectory based on case-specific drill positioning uncertainty calculations was developed. RESULTS :    The accuracy and safety of the presented software tool were validated during the conduction of eight DCA procedures performed on cadaver heads. The plan for each ear was completed in less than 20 min, and no damage to vital structures occurred during the procedures. The integrated fiducial detection functionality enabled final positioning accuracies of [Formula: see text] mm. CONCLUSIONS :    Results of this study demonstrated that the proposed software system could aid in the safe planning of a DCA tunnel within an acceptable time

Computerassistierte Präzisionschirurgie am Ohr

Chirurgische Eingriffe am Ohr stellen aufgrund der komplexen Anatomie und der Grössenverhältnisse der beteiligten anatomischen Strukturen eine Herausforderung für den HNO-Chirurgen dar. In diesem Beitrag wird ein Ansatz für die roboterbasierte Navigation zur Hörgeräteimplantation vorgestellt. Insbesondere wird auf die Möglichkeit des Fräsens von Implantatlagern im Felsenbein eingegangen. Je präziser ein Implantat im Schädel verankert werden kann, desto einfacher ist der chirurgischen Ablauf. Weiterhin, profitieren Patienten von verkürzten Operationszeiten und weniger schmerzhaften Eingriffe

Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired from 19 centers, a nnU-Net segmented the anatomy with an average Dice coefficient of 0.91 ± 0.06. For the automatic identification of inaccurate segmentations during the inference procedure, the nnU-Net framework was adapted to allow for the estimation of label-specific network uncertainty directly from its subnetworks. The average Dice coefficient of segmentation results from the subnetworks identified labels requiring segmentation correction with an average sensitivity of 1.0 and a specificity of 0.94. The presented automatic methods facilitate the use of 3D diagnosis in clinical routine by eliminating the need for time-consuming manual segmentation and slice-by-slice segmentation verification

The effect of pathological shoulder rhythm on muscle and joint forces after reverse shoulder arthroplasty, a numerical analysis.

BACKGROUND Compromised abduction ability after reverse shoulder arthroplasty is primarily linked to limited glenohumeral range of motion while scapulothoracic mobility can typically be maintained. Glenohumeral joint forces strongly depend on the resulting scapulohumeral rhythm, however, an association between the acting muscle and joint forces and the subject-specific scapulohumeral rhythm after reverse shoulder arthroplasty has not been established. METHODS Eleven reverse shoulder arthroplasty patients were divided into groups of poor and excellent abduction ability. Subject-specific models were developed and scaled for each patient using existing motion capture data in AnyBody™. Shoulder muscle and joint forces were obtained using inverse dynamics calculations during shoulder abduction to 100° in the scapula plane. The scapulohumeral rhythm, the resting abduction angle and internal body forces between the outcome groups were compared using a Mann Whitney U test. FINDINGS The mean glenohumeral and scapulothoracic contribution to overall shoulder abduction for the excellent group was on average 9.7% higher and 21.4% lower, respectively, compared to the mean of the poor group. For shoulder abduction angles between 30° and 60°, the excellent group demonstrated on average 25% higher muscle forces in the anterior deltoid which was significantly higher compared to the poor outcome patients. Scapulothoracic muscle activity did not differ significantly between the two functional groups. INTERPRETATION Accordingly, rehabilitation strategies focusing on strengthening the anterior part of the deltoid in particular may improve clinical outcomes

Automated quantification of cartilage quality for hip treatment decision support

Purpose Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools. Methods A system for the efficient automatic parametrization of the 3D hip cartilage was developed. 2D U-nets were trained on manually annotated dual-flip angle (DFA) dGEMRIC for femoral head localization and cartilage segmentation. A fully automated cartilage sectioning pipeline for analysis of central and peripheral regions, femoral-acetabular layers, and a variable number of section slices, was developed along with functionality for the automatic calculation of dGEMRIC index, thickness, surface area, and volume. Results The trained networks locate the femoral head and segment the cartilage with a Dice similarity coefficient of 88 ± 3 and 83 ± 4% on DFA and magnetization-prepared 2 rapid gradient-echo (MP2RAGE) dGEMRIC, respectively. A completely automatic cartilage analysis was performed in 18s, and no significant difference for average dGEMRIC index, volume, surface area, and thickness calculated on manual and automatic segmentation was observed. Conclusion An application for the 3D analysis of hip cartilage was developed for the automated detection of subtle morphological and biochemical signs of cartilage degradation in prognostic studies and clinical diagnosis. The segmentation network achieved a 4-time increase in processing speed without loss of segmentation accuracy on both normal and deformed anatomy, enabling accurate parametrization. Retraining of the networks with the promising MP2RAGE protocol would enable analysis without the need for B1 inhomogeneity correction in the future

Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence.

OBJECTIVE Patients with rotator cuff tears present often with glenohumeral joint instability. Assessing anatomic angles and shoulder kinematics from fluoroscopy requires labelling of specific landmarks in each image. This study aimed to develop an artificial intelligence model for automatic landmark detection from fluoroscopic images for motion tracking of the scapula and humeral head. MATERIALS AND METHODS Fluoroscopic images were acquired for both shoulders of 25 participants (N = 12 patients with unilateral rotator cuff tear, 6 men, mean (standard deviation) age: 63.7 ± 9.7 years; 13 asymptomatic subjects, 7 men, 58.2 ± 8.9 years) during a 30° arm abduction and adduction movement in the scapular plane with and without handheld weights of 2 and 4 kg. A 3D full-resolution convolutional neural network (nnU-Net) was trained to automatically locate five landmarks (glenohumeral joint centre, humeral shaft, inferior and superior edges of the glenoid and most lateral point of the acromion) and a calibration sphere. RESULTS The nnU-Net was trained with ground-truth data from 6021 fluoroscopic images of 40 shoulders and tested with 1925 fluoroscopic images of 10 shoulders. The automatic landmark detection algorithm achieved an accuracy above inter-rater variability and slightly below intra-rater variability. All landmarks and the calibration sphere were located within 1.5 mm, except the humeral landmark within 9.6 mm, but differences in abduction angles were within 1°. CONCLUSION The proposed algorithm detects the desired landmarks on fluoroscopic images with sufficient accuracy and can therefore be applied to automatically assess shoulder motion, scapular rotation or glenohumeral translation in the scapular plane. CLINICAL RELEVANCE STATEMENT This nnU-net algorithm facilitates efficient and objective identification and tracking of anatomical landmarks on fluoroscopic images necessary for measuring clinically relevant anatomical configuration (e.g. critical shoulder angle) and enables investigation of dynamic glenohumeral joint stability in pathological shoulders. KEY POINTS • Anatomical configuration and glenohumeral joint stability are often a concern after rotator cuff tears. • Artificial intelligence applied to fluoroscopic images helps to identify and track anatomical landmarks during dynamic movements. • The developed automatic landmark detection algorithm optimised the labelling procedures and is suitable for clinical application

Surgical planning tool for robotically assisted hearing aid implantation

Purpose : For the facilitation of minimally invasive robotically performed direct cochlea access (DCA) procedure, a surgical planning tool which enables the surgeon to define landmarks for patient-to-image registration, identify the necessary anatomical structures and define a safe DCA trajectory using patient image data (typically computed tomography (CT) or cone beam CT) is required. To this end, a dedicated end-to-end software planning system for the planning of DCA procedures that addresses current deficiencies has been developed. Methods : Efficient and robust anatomical segmentation is achieved through the implementation of semiautomatic algorithms; high-accuracy patient-to-image registration is achieved via an automated model-based fiducial detection algorithm and functionality for the interactive definition of a safe drilling trajectory based on case-specific drill positioning uncertainty calculations was developed. Results : The accuracy and safety of the presented software tool were validated during the conduction of eight DCA procedures performed on cadaver heads. The plan for each ear was completed in less than 20min, and no damage to vital structures occurred during the procedures. The integrated fiducial detection functionality enabled final positioning accuracies of $$0.15\pm 0.08$$ 0.15 ± 0.08 mm. Conclusions : Results of this study demonstrated that the proposed software system could aid in the safe planning of a DCA tunnel within an acceptable time

Variability in practices for drinking water vaccination of meat chickens against infectious laryngotracheitis

Context: Drinking water vaccination of young meat chickens with Infectious Laryngotracheitis (ILT) vaccine is problematic. Vaccine failure and adverse vaccine reactions are frequently reported. Variations in the technique of applying ILT vaccines by this mass vaccination method need to be understood to contribute to improving the success of vaccination. Aims: This study aimed to examine variations in the techniques of application of Infectious Laryngotracheitis vaccines via drinking water for young meat chickens. Methods: Drinking water vaccination techniques were observed and recorded across 52 broiler flocks during ILT outbreaks in three geographic areas of Australia. Descriptive statistics for all variables were computed and variations between integrator company procedures were statistically compared. Key results: Despite rigorous standard operating procedures, wide variations were observed in time of water deprivation prior to vaccination (3–15 min), time drinking water was stabilised prior to addition of vaccine and the type of stabiliser product used, time to activate the flock following filling of the water lines with vaccine (10–127 min), time for the vaccine to be consumed (36–226 min) and the volume of drinking water per bird used to provide the vaccine (11–48 mL/bird). Conclusions: Variation in vaccination technique can affect the success of drinking water vaccination against ILT in young meat chickens. Implications: Understanding the importance of the variable factors in vaccine application method can improve the success of water vaccination against ILT

Robotic middle ear access for cochlear implantation: first in man

To demonstrate the feasibility of robotic middle ear access in a clinical setting, nine adult patients with severe-to-profound hearing loss indicated for cochlear implantation were included in this clinical trial. A keyhole access tunnel to the tympanic cavity and targeting the round window was planned based on preoperatively acquired computed tomography image data and robotically drilled to the level of the facial recess. Intraoperative imaging was performed to confirm sufficient distance of the drilling trajectory to relevant anatomy. Robotic drilling continued toward the round window. The cochlear access was manually created by the surgeon. Electrode arrays were inserted through the keyhole tunnel under microscopic supervision via a tympanomeatal flap. All patients were successfully implanted with a cochlear implant. In 9 of 9 patients the robotic drilling was planned and performed to the level of the facial recess. In 3 patients, the procedure was reverted to a conventional approach for safety reasons. No change in facial nerve function compared to baseline measurements was observed. Robotic keyhole access for cochlear implantation is feasible. Further improvements to workflow complexity, duration of surgery, and usability including safety assessments are required to enable wider adoption of the procedure

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns