Leg length discrepancy following total hip replacement (THR) can contribute to poor hip function. Abnormal gait, pain, neurological disturbance and patient dissatisfaction have all been described as a result of leg length inequality after THR. The purpose of this study was to determine whether the use of computer navigation in THR can improve limb length restoration and early clinical outcomes. We performed a matched-pair study comparing 48 computer-assisted THR with 48 THRs performed using a traditional freehand alignment method. The same implant with a straight non-modular femoral stem was used in all cases. The navigation system used allowed the surgeon to monitor both acetabular cup placement and all the phases of femoral stem implantation including rasping. Patients were matched for age, sex, arthritis level, pre-operative diagnosis and pre-operative leg length discrepancy. At a minimum follow-up of six months, limb length discrepancy was measured using digital radiographs and a standardised protocol. The number of patients with a residual discrepancy of 10 mm or more and/or a postoperative over-lengthening were measured. The clinical outcome was evaluated using both the Harris Hip Score and the normalised Western Ontario and McMaster Universities (WOMAC) Arthritis Index. Restoration of limb length was significantly better in the computerassisted THR group. The number of patients with a residual limb length discrepancy greater than 10 mm and/or a post-operative over-lengthening was significantly lower. No significant difference in the Harris Hip Score or normalised WOMAC Arthritis Index was seen between the two groups. The surgical time was significantly longer in the computer-assisted THR group. No post-operative dislocations were seen.

One of the main causes of the high mortality rate in lung cancer is the late-stage tumor detection. Early diagnosis is therefore essential to increase the chances of obtaining an effective treatment quickly thus increasing the survival rate. Current screening techniques are based on imaging, with low-dose computed tomography (LDCT) as the pivotal approach. Even if LDCT has high accuracy, its invasiveness and high false positive rate limit its application to high-risk population screening. A noninvasive, cost-efficient, and easy-to-use test should instead be designed as an alternative. Exhaled breath contains thousands of volatile organic compounds (VOCs). Since ancient times, it has been understood that changes in the VOCs' mixture may be directly related to the presence of a disease, and recent studies have quantified the change in the compounds' concentration. Analyzing exhaled breath to achieve lung cancer early diagnosis represents a non-invasive, low-cost, and user-friendly approach, thus being a promising candidate for high-risk lung cancer population screening. This review discusses technological solutions that have been proposed in the literature as tools to analyze exhaled breath for lung cancer diagnosis, together with factors that potentially affect the outcome of the analysis. Even if research on this topic started many years ago, and many different technological approaches have since been adopted, there is still no validated clinical application of this technique. Standard guidelines and protocols should be defined by the medical community in order to translate exhaled breath analysis to clinical practice. Abbreviations ANNArtifical neural network AUC Area under the ROC curve CDA Canonical discriminant analysis COPD Chronic obstructive pulmonary disease EBC Exhaled breath condensate FA Factor analysis GS Gas chromatography IMS Ion mobility spectrometry LDA Linear discriminant analysis LDCT Low-dose computed tomography LOOCV Leave one out cross validation ME Mixed expiratory MS Mass spectrometry NSCLS Non-small cell lung cancer PCA Principal component analysis PPB Parts-per-billion PTR Proton transfer reaction QMB Quartz microbalance SESI Secondary electron spray ionization SIFT Selected ion flow tube RECEIVED

Real-time optical surface imaging systems offer a non-invasive way to monitor intra-fraction motion of a patient's thorax surface during radiotherapy treatments. Due to lack of point correspondence in dynamic surface acquisition, such systems cannot currently provide 3D motion tracking at specific surface landmarks, as available in optical technologies based on passive markers. We propose to apply deformable mesh registration to extract surface point trajectories from markerless optical imaging, thus yielding multi-dimensional breathing traces. The investigated approach is based on a non-rigid extension of the iterative closest point algorithm, using a locally affine regularization. The accuracy in tracking breathing motion was quantified in a group of healthy volunteers, by pair-wise registering the thoraco-abdominal surfaces acquired at three different respiratory phases using a clinically available optical system. The motion tracking accuracy proved to be maximal in the abdominal region, where breathing motion mostly occurs, with average errors of 1.09 mm. The results demonstrate the feasibility of recovering multi-dimensional breathing motion from markerless optical surface acquisitions by using the implemented deformable registration algorithm. The approach can potentially improve respiratory motion management in radiation therapy, including motion artefact reduction or tumour motion compensation by means of internal/external correlation models.

This paper describes methods and experimental studies concerned with quantitative reconstruction of finger movements in real-time, by means of multi-camera system and 24 surface markers. The approach utilizes a kinematic model of the articulated hand which consists in a hierarchical chain of rigid body segments characterized by 22 functional degrees of freedom and the global roto-translation. This work is focused on the experimental evaluation of a kinematical hand model for biomechanical analysis purposes. From a static posture, a completely automatic calibration procedure, based on anthropometric measures and geometric constraints, computes axes, and centers of rotations which are then utilized as the base of an interactive real-time animation of the hand model. The motion tracking, based on automatic marker labeling and predictive filter, is empowered by introducing constraints from functional finger postures. The validation is performed on four normal subjects through different right-handed motor tasks involving voluntary flexion-extension of the thumb, voluntary abduction-adduction of the thumb, grasping, and finger pointing. Performances are tested in terms of repeatability of angular profiles, model-based ability to predict marker trajectories and tracking success during real-time motion estimation. Results show intra-subject repeatability of the model calibration both to different postures and to re-marking in the range of 0.5 and 2 mm, respectively. Kinematic estimation proves satisfactory in terms of prediction capability (index finger: maximum RMSE 2.02 mm; thumb: maximum RMSE 3.25 mm) and motion reproducibility (R (2) coefficients--index finger: 0.96, thumb: 0.94). During fast grasping sequence (60 Hz), the percentage of residual marker occlusions is less than 1% and processing and visualization frequency of 50 Hz confirms the real-time capability of the motion estimation system.

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