Ensemble detection of hand joint ankylosis and subluxation in radiographic images using deep neural networks

The modified total Sharp score (mTSS) is often used as an evaluation index for joint destruction caused by rheumatoid arthritis. In this study, special findings (ankylosis, subluxation, and dislocation) are detected to estimate the efficacy of mTSS by using deep neural networks (DNNs). The proposed method detects and classifies finger joint regions using an ensemble mechanism. This integrates multiple DNN detection models, specifically single shot multibox detectors, using different training data for each special finding. For the learning phase, we prepared a total of 260 hand X-ray images, in which proximal interphalangeal (PIP) and metacarpophalangeal (MP) joints were annotated with mTSS by skilled rheumatologists and radiologists. We evaluated our model using five-fold cross-validation. The proposed model produced a higher detection accuracy, recall, precision, specificity, F-value, and intersection over union than individual detection models for both ankylosis and subluxation detection, with a detection rate above 99.8% for the MP and PIP joint regions. Our future research will aim at the development of an automatic diagnosis system that uses the proposed mTSS model to estimate the erosion and joint space narrowing score.

Diagnosis and treatment of flexor tendon injuries of the hand: what the radiologist needs to know

This article reviews the diagnosis and treatment of flexor tendon injuries of the hand highlighting flexor tendon anatomy, important pre-operative imaging findings, surgical options, and post-operative complications. Imaging plays a key role in guiding treatment of these difficult to manage injuries. Thus, it is important for radiologists to have a sound understanding of factors important in treatment decision-making. In the pre-operative setting, accurately identifying the location of the torn proximal tendon stump in subacute and chronic injuries helps dictate whether the patient is a candidate for a primary flexor tendon repair or may require a tendon reconstruction to restore function. In the post-operative setting, the status of the repair and presence of surrounding adhesions help dictate if and when the patient will require subsequent surgery and whether that surgery will be a tenolysis, revision repair, reconstruction, or fusion.

The Addition of Hand-specific Skeletal Maturity Parameters Does Not Improve Skeletal Maturity Estimation Accuracy of the Modified Fels Wrist System

BACKGROUND

The Modified Fels Wrist system is potentially the most accurate clinically accessible skeletal maturity system utilizing hand or wrist radiographs. During development, parameters distal to the metacarpals were excluded. We attempted to further optimize the Modified Fels wrist system through the inclusion of hand parameters distal to the metacarpals.

METHODS

Forty-three new anteroposterior (AP) hand radiographic parameters were identified from the Fels and Greulich and Pyle (GP) skeletal maturity systems. Twelve parameters were eliminated from further evaluation for poor correlation with skeletal maturity, poor reliability, and lack of relevance in the peripubertal years. In addition to the 8 previously described Modified Fels Wrist parameters, 31 hand radiographic parameters were evaluated on serial peripubertal AP hand radiographs to identify the ones most important for accurately estimating skeletal age. This process produced a "Modified Fels hand-wrist" model; its performance was compared with (1) GP only; (2) Sanders Hand (SH) only; (3) age, sex, and GP; (4) age, sex, and SH; and (5) Modified Fels Wrist system.

RESULTS

Three hundred seventy-two radiographs from 42 girls and 38 boys were included. Of the 39 radiographic parameters that underwent full evaluation, 9 remained in the combined Modified Fels Hand-Wrist system in addition to chronological age and sex. Four parameters are wrist specific, and the remaining 5 are hand specific. The Hand-Wrist system outperformed both GP and SH in estimating skeletal maturity ( P <0.001). When compared with the Modified Fels Wrist system, the Modified Fels Hand-Wrist system performed similarly regarding skeletal maturity estimation (0.36±0.32 vs. 0.34±0.26, P =0.59) but had an increased (worse) rate of outlier predictions >1 year discrepant from true skeletal maturity (4.9% vs. 1.9%, P =0.01).

CONCLUSIONS

The addition of hand parameters to the existing Modified Fels Wrist system did not improve skeletal maturity estimation accuracy and worsened the rate of outlier estimations. When an AP hand-wrist radiograph is available, the existing Modified Fels wrist system is best for skeletal maturity estimation.

LEVEL OF EVIDENCE

Level III.

Relationship Between Hand Function and Handheld Ultrasound Imaging in Inclusion Body Myositis

OBJECTIVE

Ultrasound studies in inclusion body myositis (IBM) have reported a characteristic pattern of increased echointensity in the flexor digitorum profundus (FDP) with relative sparing of the flexor carpi ulnaris (FCU). We examined the relationship between echointensity of the FDP and FCU muscles and hand strength or patient-reported outcomes (PROs).

METHODS

A total of 15 patients with IBM were recruited. Ultrasound images of the FDP and FCU muscles were obtained by a point-of-care ultrasound and graded using the modified Heckmatt score. Hand grip and neutral pinch strength were measured by dynamometry. PROs were assessed by the IBM Upper Extremity Function Scale.

RESULTS

FDP and/or FCU modified Heckmatt score showed a significant relationship with grip, neutral pinch strength, and PROs.

CONCLUSIONS

Point-of-care ultrasound examination of the forearm may serve as an extension of the neuromuscular examination. The semi-qualitative echointensity rating based on modified Heckmatt score seems to correlate well with the objective strength measurement and PROs.

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females

Predicting a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace. Often, convolutional neural network (CNN) models are built using images of the face as biometric data. However, hands hold telltale signs of a person's age. To determine the utility of using only hand images in predicting CA, we developed two deep CNNs based on 1) dorsal hand images (H) and 2) frontal face images (F). Subjects (n = 1454) were Indian women, 20-80 years, across three geographic cohorts (Mumbai, New Delhi and Bangalore) and having a broad variation in skin tones. Images were randomised: 70% of F and 70% of H were used to train CNNs. The remaining 30% of F and H were retained for validation. CNN validation showed mean absolute error for predicting CA using F and H of 4.1 and 4.7 years, respectively. In both cases correlations of predicted and actual age were statistically significant (r(F) = 0.93, r(H) = 0.90). The CNNs for F and H were validated for dark and light skin tones. Finally, by blurring or accentuating visible features on specific regions of the hand and face, we identified those features that contributed to the CNN models. For the face, areas of the inner eye corner and around the mouth were most important for age prediction. For the hands, knuckle texture was a key driver for age prediction. Collectively, for AI estimates of CA, CNNs based solely on hand images are a viable alternative and comparable to CNNs based on facial images.

The lateral approach water bath: A novel method of ultrasound imaging of the hand

BACKGROUND

Traditional water baths for ultrasound exams place a hand into a pan of water and submerge an ultrasound probe into the water. While this improves ultrasound transmission and moves structures into the focal zone to make higher resolution images, this method does have limitations. Patients must be manipulated directly under the probe, which can be limited by pain or normal movement restrictions. The probe must also be held very still in water to minimize motion artifact. The lateral approach water bath method addresses such limitations by imaging through the side of a thin-walled plastic container without submerging the probe. This reduces much need for patient manipulation by imaging through the side of a column-shaped bath, which has 360 degrees of imaging freedom. It also stabilizes the probe directly against the flat, firm container to reduce image degrading motion artifact. We hypothesized that because of these improvements the lateral approach water bath might create higher quality images than traditional water baths.

METHODS

We compared twenty images from each method, which were obtained with the same model and ultrasound operator at the same time. Two ultrasound fellowship trained blinded reviewers rated the images for quality and adequacy for clinical decision making on a scale from 1 to 5.

RESULTS

Image quality was better for the lateral water bath, with an average rating of 4.2 compared to the traditional bath's 2.6 (p < 0.001). Adequacy to aid clinical decision making was better for the lateral approach bath with an average rating of 4.0 compared to the traditional bath's 2.6 (p < 0.001). The lateral bath also had a smaller range for image quality and thus greater consistency.

CONCLUSIONS

The lateral approach water bath is a method of hand imaging that produces higher quality, more consistent, and more clinically useful images than traditional water bath imaging.

Lipofibromatous Hamartoma of the Median Nerve - A Rare Condition in the Hand

Lipofibromatous hamartoma (LFH) of the median nerve is a rare condition in the hand and often remains asymptomatic for a significant period. MRI imaging can reveal unique tumour characteristics; however, the definitive diagnosis is confirmed through a tissue biopsy. In this report, a 38-year-old male presented with a gradually growing mass on his right hand. Physical examination revealed a large soft tissue mass extending from the thenar area to the wrist, causing compression of the median nerve. MRI confirmed the presence of a distinct soft tissue mass on the volar side of the hand. The mass was excised along with a fascicle and confirmed by histological examination. One year after surgery, sensation has improved, but weakness remains and opponensplasty was offered to the patient. Although the treatment strategy of LFH of the median nerve remains controversial, delayed treatment can result in severe compressive neuropathy and irreversible nerve damage. Level of Evidence: Level V (Therapeutic).

High Performance Wearable Ultrasound as a Human-Machine Interface for Wrist and Hand Kinematic Tracking

OBJECTIVE

Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. Ultrasound (US) has received increasing attention as an alternative to sEMG-based HMIs. Here, we developed a portable US armband system with 24 channels and a multiple receiver approach, and compared it with existing sEMG- and US-based HMIs on movement intention decoding.

METHODS

US and motion capture data was recorded while participants performed wrist and hand movements of four degrees of freedom (DoFs) and their combinations. A linear regression model was used to offline predict hand kinematics from the US (or sEMG, for comparison) features. The method was further validated in real-time for a 3-DoF target reaching task.

RESULTS

In the offline analysis, the wearable US system achieved an average [Formula: see text] of 0.94 in the prediction of four DoFs of the wrist and hand while sEMG reached a performance of [Formula: see text]= 0.60. In online control, the participants achieved an average 93% completion rate of the targets.

CONCLUSION

When tailored for HMIs, the proposed US A-mode system and processing pipeline can successfully regress hand kinematics both in offline and online settings with performances comparable or superior to previously published interfaces.

SIGNIFICANCE

Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The wearable US system allowed for robust proportional and simultaneous control over multiple DoFs in both offline and online settings.

The Implementation of a Gesture Recognition System with a Millimeter Wave and Thermal Imager

During the COVID-19 pandemic, the number of cases continued to rise. As a result, there was a growing demand for alternative control methods to traditional buttons or touch screens. However, most current gesture recognition technologies rely on machine vision methods. However, this method can lead to suboptimal recognition results, especially in situations where the camera is operating in low-light conditions or encounters complex backgrounds. This study introduces an innovative gesture recognition system for large movements that uses a combination of millimeter wave radar and a thermal imager, where the multi-color conversion algorithm is used to improve palm recognition on the thermal imager together with deep learning approaches to improve its accuracy. While the user performs gestures, the mmWave radar captures point cloud information, which is then analyzed through neural network model inference. It also integrates thermal imaging and palm recognition to effectively track and monitor hand movements on the screen. The results suggest that this combined method significantly improves accuracy, reaching a rate of over 80%.

Two arterial variations of the hand and wrist present bilaterally (persistent median artery and superficial dorsal branch of the radial artery): ultrasound findings

PURPOSE

Arterial variations of the upper limb may bear high importance for many clinical procedures, including the use of flaps in plastic surgery. We present a feasible way for visualization and confirmation of presence of these variations.

METHODS

All variations were detected by ultrasonography and confirmed by Color Doppler Imaging. Proper documentation was taken in order to present our findings.

RESULTS

We report a case of a 19-year-old female who showed two concomitant arterial variations of the forearm and the hand bilaterally. These two variations were the persistent median artery and the superficial dorsal branch of the radial artery which both significantly contributed to the blood supply of the hand. All examinations were performed by the same investigator and all findings were reviewed by an experienced sonographist.

CONCLUSION

An unusual arrangement of the arterial system can be easily detected. We present a feasible way to prevent iatrogenic injuries and increase utilization of anatomical variants knowledge in surgery by using ultrasound prior to planning surgical procedures.

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

Occupational radiation dose on the hand of assisting medical staff in diagnostic CT scans

Chronic radiation exposure increases the risk of skin damage of medical personnel engaged in radiology. However, hand dose measurements in computed tomography (CT) for diagnostic purposes have not been evaluated. The occupational radiation dose to the hands of CT assistants was herein investigated to evaluate its compliance with the equivalent dose limit for the hand (500 mSv/year). The occupational doses of nine CT assistants were measured in 89 cases (April 2017-May 2018) by installing radio-photoluminescence glass dosemeters (GD-302 M) (70-μm dose-equivalent conversion coefficient = 0.37) on the dorsal aspect of both hands. The occupational dose to the hand was the highest with head holding (right: 1.14 mSv/CT scan, left: 1.07 mSv/CT scan). Considering the results for annual work, even for head holding, the hand dose of the CT-assisting personnel was insignificant. However, CT assistants should be mindful of the possibility of locally higher doses to hands.

A method for Kashin-Beck disease auxiliary diagnosis based on the features in regions of the potential lesion

BACKGROUND

Kashin-Beck disease (KBD) is a severe arthropathy that causes deformity. Patients with advanced stages of KBD often show symptoms, such as short stature. Early-stage diagnosis and treatment can effectively prevent the disease from worsening. Diagnosis of early-stage patients is usually made by X-ray examination. However, the time-consuming image recognition and the lack of professional doctors may delay the patient's condition. Therefore, a method that can efficiently complete the auxiliary diagnosis is necessary.

PURPOSE

This study presents a KBD auxiliary diagnosis method based on radiographs, which uses deep learning to locate potential lesion regions and extract features for accurate diagnosis.

METHODS

This work presents a method that relies on hand radiographs to locate eight regions of the potential lesion (RoPL) and finally make the KBD auxiliary diagnosis. The localization of RoPL is achieved through a two-step model, with the first step predicting a rough location and a deep convolutional neural network (DCNN) with attention mechanism used to generate precise center coordinates based on the previous step's results. Based on the localization result, regional features are extracted, which provides information about the joints and textures of RoPL from a finer granularity. Another DCNN is utilized to obtain general features from hand radiographs, which provide morphological and structural information about the entire hand bone These features offer a concatenated feature for categorization to raise accuracy. A doctor-like approach is adopted to diagnose based on regional features to enhance performance, and a final decision is made using a vote that considers diagnostic outcomes from all aspects. The dataset used in our study was collected by our research team in KBD-endemic areas of Tibet since 2017, containing 373 diseased and 764 normal images.

RESULT

Our model guarantees that over 95% of the predicted coordinates are within five pixels of the real coordinates according to Euclidean distance. The accuracy of the diagnostic network achieved 91.3%, with precision and recall achieving 83% and 87%, respectively. Compared to the approach without exact localization of the illness region on the same test set, our method achieved a roughly 6% increase in accuracy and nearly 30% increase in recall rate.

CONCLUSIONS

Based on hand radiographs, this study suggests a novel method for KBD diagnosis. The high-precision localization network guarantees precise extraction of lesion-prone regional features, and the multi-scale features and innovative classification method further enhance model performance compared to related approaches.

Measurement of the lumbrical muscle activity of the hand using electromyography supported by the ultrasound imaging technique with string navigation

Although placing surface electrodes on small muscles by palpation is difficult, ultrasound guidance may enable electrode placement on the small muscles. This study aimed to examine whether ultrasound guidance is helpful for placement of electrodes on a small muscle, such as the hand lumbrical muscle. Twelve dominant hands of 12 healthy right-handed adults were included in this study. The first lumbrical muscle belly of the hands was identified using ultrasound guidance with a string navigation technique for placing surface electrodes. This technique was designed to identify the location of the center of the muscle belly under ultrasound imaging using a string. After the electrodes were placed on the muscle belly using this technique, the surface electromyographic signals of the first lumbrical, first dorsal interosseous, and adductor pollicis muscles were recorded. The activity of the lumbrical muscle could be separately measured of the first dorsal interosseous and adductor pollicis muscles. This technique has the potential to enable surface electromyography of small muscles for which placement of surface electrodes by palpation is challenging.

Folate deficiency is associated with increased radiographic severity of osteoarthritis in knee joints but not in hand joints

OBJECTIVES

No previous studies have explored the effect of folate deficiency on the severity of osteoarthritis (OA). Therefore, we investigated the relationship between folate level and features on knee and hand radiographs in a large, population-based OA cohort.

METHODS

Among 9,260 subjects enrolled in the Dong-gu study, 2,489 who had knee and hand joint radiographs were included. Of these, subjects with a history of amputation or total knee replacement were excluded. Serum folate levels were measured using blood samples collected at the time of enrolment and stored. A semi-quantitative system was used to grade the severity of hand and knee x-ray changes. Linear regression was performed to assess relationships between serum folate levels and knee and hand radiographic scores after adjusting for age, sex, body mass index, smoking, alcohol consumption, education, physical activity, occupation, vitamin D, and ferritin.

RESULTS

A total of 2,322 subjects were recruited. After adjusting for confounders, participants with folate deficiency (<4 ng/mL) had higher total (p<0.001), osteophyte (p<0.001), joint space narrowing (p=0.002), tibial attrition (p<0.001), and sclerosis (p=0.005) scores for knee joint radiographs compared to participants with a normal folate level. After adjusting for confounders, the radiographic scores for hand joints did not differ between the groups.

CONCLUSIONS

Folate deficiency is associated with increased radiographic severity of OA in knee joints, but not in hand joints. Further studies are needed to explore the differential effects of folate on the severity of knee and hand OA.

Recognition Performance Analysis of a Multimodal Biometric System Based on the Fusion of 3D Ultrasound Hand-Geometry and Palmprint

Multimodal biometric systems are often used in a wide variety of applications where high security is required. Such systems show several merits in terms of universality and recognition rate compared to unimodal systems. Among several acquisition technologies, ultrasound bears great potential in high secure access applications because it allows the acquisition of 3D information about the human body and is able to verify liveness of the sample. In this work, recognition performances of a multimodal system obtained by fusing palmprint and hand-geometry 3D features, which are extracted from the same collected volumetric image, are extensively evaluated. Several fusion techniques based on the weighted score sum rule and on a wide variety of possible combinations of palmprint and hand geometry scores are experimented with. Recognition performances of the various methods are evaluated and compared through verification and identification experiments carried out on a homemade database employed in previous works. Verification results demonstrated that the fusion, in most cases, produces a noticeable improvement compared to unimodal systems: an EER value of 0.06% is achieved in at least five cases against values of 1.18% and 0.63% obtained in the best case for unimodal palmprint and hand geometry, respectively. The analysis also revealed that the best fusion results do not include any combination between the best scores of unimodal characteristics. Identification experiments, carried out for the methods that provided the best verification results, consistently demonstrated an identification rate of 100%, against 98% and 91% obtained in the best case for unimodal palmprint and hand geometry, respectively.

Measuring fascicle lengths of extrinsic and intrinsic thumb muscles using extended field-of-view ultrasound

Complex motion of the human thumb is enabled by the balanced architectural design of the extrinsic and intrinsic thumb muscles. Given that recent imaging advances have not yet been applied to enhance our understanding of the in vivo properties of thumb muscles, the objective of this study was to test the reliability and validity of measuring thumb muscle fascicle lengths using extended field of view ultrasound (EFOV-US). Three muscles (FPL: flexor pollicis longus, APB: abductor pollicis brevis, and ECU: extensor carpi ulnaris) were imaged in eight healthy adults (4 female; age, 21.6 ± 1.3 years; height, 175.9 ± 8.3 cm)[mean ± SD]. Measured fascicle lengths were compared to cadaveric data (all muscles) and ultrasound data (ECU only). Additionally, to evaluate how fascicle lengths scale with anthropometric measurements, height, forearm length, hand length, and hand width were recorded. The EFOV-US method obtained precise fascicle length measurements [mean ± SD] for the FPL (6.2 ± 0.5 cm), APB (5.1 ± 0.3 cm), and ECU (4.0 ± 0.4 cm). However, our EFOV-US measurements were consistently different (p < 0.05) than prior cadaveric data, highlighting the need to better understand differences between in vivo and ex vivo fascicle length measurements. Fascicle length was significantly related to only hand length (r2 = 0.56, p = 0.03) for APB, highlighting that anthropometric scaling may not accurately estimate thumb muscle length. As the first study to apply EFOV-US to measure thumb muscle fascicle lengths, this study expands the utility of this imaging technology within the upper limb.

Analysis of whole-body MRI artifacts in a pediatric population with a special emphasis on the effect of hands position

PURPOSE

The purpose of this retrospective study was to determine the prevalence of artifacts on whole-body (WB) magnetic resonance imaging (MRI) examination in pediatric patients and identify their causes.

MATERIALS AND METHODS

A total of 107 pediatric patients who underwent a total of 107 WB-MRI examinations, including short-tau inversion recovery (STIR) and T1-weighted sequences, were included. There were 62 girls and 45 boys with a mean age of 11 ± 3 (SD) years (age range: 2-16 years). WB-MRI examinations were analyzed for the presence of artifacts on STIR and T1-weighted sequences. Artifacts were further assigned to one of eight categories (motion, partial volume, cross-talk, phase sampling, susceptibility, equipment, noise, and "other") and 19 anatomical sites by a 4-year resident. Prevalence of artifacts were analyzed especially according to hands position during the examination for the upper limbs and patients' age. Age was expressed as a binary variable using median age (10 years) as the cut-off value. All qualitative variables were compared using chi-square test.

RESULTS

A total of 3436 artifacts were found. The STIR sequences showed more "noise" artifacts (93/1038; 8.96%) and more "cross-talk" (102/1038; 9.83%) artifacts than T1-weighted sequences (12/1038 [1.16%] and 7/1038 [0.67%], respectively) (P < 0.001 for both). T1-weighted sequences showed more "equipment" (84/1038; 8.09%) and "stair-step" (a subset of "other") (41/1038; 3.95%) artifacts than the STIR sequences (39/1038 [3.76%] and 21/1038 [2.02%], respectively) (P < 0.001 and P = 0.01, respectively). T1-weighted sequences showed fewer artifacts on the wrists when the hands were under the bottom (P = 0.001). T1-weighted sequences showed less "equipment" artifacts when the hands were alongside the body (22/296; 7%) than on the abdomen (48/432; 11%) or under the bottom (14/128; 11%) (P < 0.001). STIR sequences showed more "motion" artifacts when the hands were on the abdomen (54/432; 13%) than alongside the body (30/296; 10%) or under the bottom (15/128; 12%) (P < 0.001). WB-MRI examinations had more "susceptibility" artifacts (38/960; 4%) and more "equipment" artifacts (81/960; 8.4%) in patients older than 10 years than in those under 10 years (23/752 [3.1%] and 42/752 [5.6%]) (P = 0.01 and P < 0.001, respectively).

CONCLUSION

Artifacts on WB-MRI do not affect coronal STIR and T1-weighted sequences equally, so the use of both sequence types appears useful. Hands position should be considered with respect to both diagnostic benefit and safety.

Fetal Hands: A Comprehensive Review of Prenatal Assessment and Diagnosis Over the Past 40 Years

Fetal skeletal dysplasias involving limbs and hands are rare congenital malformations. Prenatal two-dimensional ultrasound diagnosis of fetal limb defects has a sensitivity of about 30%; however, an increased detection rate may be obtained using three-dimensional (3-D) ultrasound in the rendering mode. 3-D ultrasound may be used as a complementary method providing additional information. Currently, magnetic resonance imaging (MRI), with the emergence of ultrafast imaging techniques and new sequences, allows for better diagnosis of several fetal skeletal dysplasias such as limb reduction defects and neuromuscular disorders. 3-D volumetric images from ultrasound or MRI scan data allow 3-D ultrasound reconstructions of virtual/physical models, and virtual reality can help researchers to improve our understanding of both normal and abnormal fetal limb/hand anatomy. In this article, we review the embryological development of fetal hands and their main anomalies including prenatal diagnostic methods, genetic counseling, the role of orthopedic and plastic surgery reconstruction, and new perspectives in fetal surgery.

[Radiological methods for age diagnostics : Clinical and forensic aspects]

Age diagnostics play an increasing role in radiology. Medical and forensic questions are indications for the application of age diagnostics. In addition to X‑rays of the hand in childhood, panoramic tomography and computed tomography are currently the standard procedures. Alternative modalities without ionizing radiation (sonography, magnetic resonance imaging) have not (yet) been established. The purpose of this article is to present the indications and methods of age diagnostics in the clinical and forensic contexts and to familiarize you with their advantages and disadvantages as well as the possibility of determining the final length.

Diagnostic Reference Levels for Common Orthopedic Hand and Wrist Procedures Using Intraoperative Mini C-arm Fluoroscopy

BACKGROUND

Diagnostic reference levels are radiation dose levels in medical radiodiagnostic practices for typical examinations for groups of standard-sized individuals for broadly defined types of equipment. This study aimed to contribute to national diagnostic reference levels for common hand and wrist procedures using mini C-arm fluoroscopy. Small joint and digital fracture procedure diagnostic reference levels have not been reported in significant numbers previously with procedure-level stratification.

METHODS

Data were collected from fluoroscopy logbooks and were cross-referenced against the audit log kept on fluoroscopy machines. A total of 603 procedures were included.

RESULTS

The median radiation dose for wrist fracture open fixation was 2.73 cGycm2, Kirschner wiring (K-wiring) procedures was 2.36 cGycm2, small joint arthrodesis was 1.20 cGycm2, small joint injections was 0.58 cGycm2, and phalangeal fracture fixation was 1.05 cGycm2.

CONCLUSIONS

Wrist fracture fixation used higher radiation doses than phalangeal fracture fixation, arthrodeses, and injections. Injections used significantly less radiation than the other procedures. There are significant differences in total radiation doses when comparing these procedures in hand and wrist surgery. National and international recommendations are that institutional audit data should be collected regularly and should be stratified by procedure type. This study helps to define standards for this activity by adding to the data available for wrist fracture diagnostic reference levels and defining standards for digital and injection procedures.

Ultrasound Probe and Hand-Eye Calibrations for Robot-Assisted Needle Biopsy

In robot-assisted ultrasound-guided needle biopsy, it is essential to conduct calibration of the ultrasound probe and to perform hand-eye calibration of the robot in order to establish a link between intra-operatively acquired ultrasound images and robot-assisted needle insertion. Based on a high-precision optical tracking system, novel methods for ultrasound probe and robot hand-eye calibration are proposed. Specifically, we first fix optically trackable markers to the ultrasound probe and to the robot, respectively. We then design a five-wire phantom to calibrate the ultrasound probe. Finally, an effective method taking advantage of steady movement of the robot but without an additional calibration frame or the need to solve the AX=XB equation is proposed for hand-eye calibration. After calibrations, our system allows for in situ definition of target lesions and aiming trajectories from intra-operatively acquired ultrasound images in order to align the robot for precise needle biopsy. Comprehensive experiments were conducted to evaluate accuracy of different components of our system as well as the overall system accuracy. Experiment results demonstrated the efficacy of the proposed methods.

HandVoxNet++: 3D Hand Shape and Pose Estimation Using Voxel-Based Neural Networks

3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. Existing methods addressing it directly regress hand meshes via 2D convolutional neural networks, which leads to artifacts due to perspective distortions in the images. To address the limitations of the existing methods, we develop HandVoxNet++, i.e., a voxel-based deep network with 3D and graph convolutions trained in a fully supervised manner. The input to our network is a 3D voxelized-depth-map-based on the truncated signed distance function (TSDF). HandVoxNet++ relies on two hand shape representations. The first one is the 3D voxelized grid of hand shape, which does not preserve the mesh topology and which is the most accurate representation. The second representation is the hand surface that preserves the mesh topology. We combine the advantages of both representations by aligning the hand surface to the voxelized hand shape either with a new neural Graph-Convolutions-based Mesh Registration (GCN-MeshReg) or classical segment-wise Non-Rigid Gravitational Approach (NRGA++) which does not rely on training data. In extensive evaluations on three public benchmarks, i.e., SynHand5M, depth-based HANDS19 challenge and HO-3D, the proposed HandVoxNet++ achieves the state-of-the-art performance. In this journal extension of our previous approach presented at CVPR 2020, we gain 41.09% and 13.7% higher shape alignment accuracy on SynHand5M and HANDS19 datasets, respectively. Our method is ranked first on the HANDS19 challenge dataset (Task 1: Depth-Based 3D Hand Pose Estimation) at the moment of the submission of our results to the portal in August 2020.

Clustering-Driven DGS-Based Micro-Doppler Feature Extraction for Automatic Dynamic Hand Gesture Recognition

We propose in this work a dynamic group sparsity (DGS) based time-frequency feature extraction method for dynamic hand gesture recognition (HGR) using millimeter-wave radar sensors. Micro-Doppler signatures of hand gestures show both sparse and structured characteristics in time-frequency domain, but previous study only focus on sparsity. We firstly introduce the structured prior when modeling the micro-Doppler signatures in this work to further enhance the features of hand gestures. The time-frequency distributions of dynamic hand gestures are first modeled using a dynamic group sparse model. A DGS-Subspace Pursuit (DGS-SP) algorithm is then utilized to extract the corresponding features. Finally, the support vector machine (SVM) classifier is employed to realize the dynamic HGR based on the extracted group sparse micro-Doppler features. The experiment shows that the proposed method achieved 3.3% recognition accuracy improvement over the sparsity-based method and has a better recognition accuracy than CNN based method in small dataset.

[Postoperative imaging of the musculoskeletal system - hand]

BACKGROUND

Postoperative imaging is essential to document the surgical outcome of the musculoskeletal system of the hand.

OBJECTIVES

Common radiological findings in the postoperative musculoskeletal system of the hand are explained in the context of the preoperative findings.

MATERIALS AND METHODS

For important surgical procedures of the hand, classifications, surgical procedures, and complications are presented. Diagnostic criteria in postoperative radiography and computed tomography (CT) imaging are described for each indication group.

RESULTS

Clinical information and postoperative findings are systematically presented for fractures of the radius, scaphoid, metacarpal and phalanges, scapholunate dissociation, perilunate injuries and for procedures of arthrodesis and arthroplasty in the hand. Complications are pointed out.

DISCUSSION

Precise reporting in postoperative radiography and CT imaging of the hand is based on a well-defined order, a standardised examination technique and on synoptic evaluation of clinical and radiological criteria.

Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography

Electrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with static gestures, dynamic gestures are more informative and can achieve more functions in human-machine collaboration. In order to verify the feasibility of dynamic gesture recognition based on EIT, a traditional excitation drive pattern is optimized in this paper. The drive pattern of the fixed excitation electrode is tested for the first time to simplify the measurement process of the dynamic gesture. To improve the recognition accuracy of the dynamic gestures, a dual-channel feature extraction network combining a convolutional neural network (CNN) and gated recurrent unit (GRU), namely CG-SVM, is proposed. The new center distance loss is designed in order to simultaneously supervise the intra-class distance and inter-class distance. As a result, the discriminability of the confusing data is improved. With the new excitation drive pattern and classification network, the recognition accuracy of different interference data has increased by 2.7~14.2%. The new method has stronger robustness, and realizes the dynamic gesture recognition based on EIT for the first time.

A Wearable Ultrasound Interface for Prosthetic Hand Control

Ultrasound can non-invasively detect muscle deformations and has great potential applications in prosthetic hand control. Traditional ultrasound equipment was usually too bulky to be applied in wearable scenarios. This research presented a compact ultrasound device that could be integrated into a prosthetic hand socket. The miniaturized ultrasound system included four A-mode ultrasound transducers for sensing musculature deformations, a signal excitation/acquisition module, and a prosthetic hand control module. The size of the ultrasound system was 65*75*25mm, weighing only 85g. For the first time, we integrated the ultrasound system into a prosthetic hand socket to evaluate its performance in practical prosthetic hand control. We designed an experiment requiring twenty subjects to perform six commonly used gestures. The performance of decoding ultrasound signals was analyzed offline using four classification algorithms and then was assessed in online control. The average values of online classification accuracy with and without wearing the physical prosthetic were 91.5 ±6.4% and 96.5 ±3.0%, respectively. We found that wearing the prosthetic hand influenced the ultrasound gestures classification accuracy, but remarkable online classification performance could still be maintained. These experimental results demonstrated the efficacy of the designed integrated ultrasound system for practical use, paving the way for an effective HMI system that could be widely used in prosthetic hand control.

A Dual-Branch Self-Boosting Framework for Self-Supervised 3D Hand Pose Estimation

Although 3D hand pose estimation has made significant progress in recent years with the development of the deep neural network, most learning-based methods require a large amount of labeled data that is time-consuming to collect. In this paper, we propose a dual-branch self-boosting framework for self-supervised 3D hand pose estimation from depth images. First, we adopt a simple yet effective image-to-image translation technology to generate realistic depth images from synthetic data for network pre-training. Second, we propose a dual-branch network to perform 3D hand model estimation and pixel-wise pose estimation in a decoupled way. Through a part-aware model-fitting loss, the network can be updated according to the fine-grained differences between the hand model and the unlabeled real image. Through an inter-branch loss, the two complementary branches can boost each other continuously during self-supervised learning. Furthermore, we adopt a refinement stage to better utilize the prior structure information in the estimated hand model for a more accurate and robust estimation. Our method outperforms previous self-supervised methods by a large margin without using paired multi-view images and achieves comparable results to strongly supervised methods. Besides, by adopting our regenerated pose annotations, the performance of the skeleton-based gesture recognition is significantly improved.

[Ganglion cysts on wrists and hands]

The ganglion cyst is the most common soft-tissue tumour of the hand and wrist. 60-70% are found dorsally on the wrist. Ultrasound and MRI-imaging can distinguish whether the tumour is cystic or solid and may be helpful in making a diagnosis. This article reviews the different treatment techniques and rates of recurrence. Arthroscopic excision has shown promising results, but open excision remains the gold standard. The aetiology and pathogenesis of the condition is still unknown and further research is needed especially in reducing the risk of recurrence.

Candida albicans tenosynovitis of the hand

An immunosuppressed 72 year-old woman presented with bilateral hand-fingers tenderness and increased circumference. Serum leukocyte and neutrophil counts were slightly raised and C-reactive protein moderately increased. On ultrasound there was flexor tenosynovitis of the 3rd right and 1st to 4th left hand-fingers and left common flexor tendon sheet. Candida albicans was isolated in synovial fluid cultures and the symptoms resolved with fluconazole. This is the first case reporting bilateral Candida tenosynovitis of the hand, highlighting the possible role of concealed hematogenous spread of opportunistic microorganisms in atypical clinical manifestations in immunocompromised patients.

Three-Dimensional Carpal Arch Morphology Using Robot-Assisted Ultrasonography

OBJECTIVE

The morphology of the carpal arch implicates the available space for the median nerve within the carpal tunnel. The purposes of this study were to 1) reconstruct the three-dimensional (3D) carpal arch by robot-assisted ultrasonography with a linear array transducer using cadaveric hands, and 2) investigate the 3D morphological properties of the carpal arch.

METHODS

An ultrasound probe with two-dimensional (2D) linear array was integrated on a robotic arm and maneuvered over the cadaveric carpal tunnels to scan the entire transverse carpal ligament and its osseous attachments to carpal bones. The acquired series of 2D ultrasound images together with robot positioning were utilized to reconstruct the 3D carpal arch for morphometric analyses.

RESULTS

Total carpal arch volume was 1099.4 ± 163.2 mm³ with the distal, middle, and proximal regions contributing 18.2 ± 1.5%, 32.7 ± 1.2%, and 49.1 ± 2.3%, respectively. The ligament surface area was 420.1 ± 63.9 mm². The carpal arch width, height, curvature, length, area, and palmar bowing index progressively increased from the distal to proximal locations within the tunnel (p < 0.01).

CONCLUSION

The incorporation of the robot technology with the ultrasound system advanced the applications of traditional 2D ultrasound imaging for a 3D carpal arch reconstruction, allowing for comprehensive morphological assessment of the carpal arch.

SIGNIFICANCE

The developed workflow can be used for the reconstruction and analysis of other anatomical features in vivo.

Development of radiographic classification criteria for hand osteoarthritis: a methodological report (Phase 2)

OBJECTIVES

In Phase 1 of developing new hand osteoarthritis (OA) classification criteria, features associated with hand OA were identified in a population with hand complaints. Radiographic findings could better discriminate patients with hand OA and controls than clinical examination findings. The objective of Phase 2 was to achieve consensus on the features and their weights to be included in three radiographic criteria sets of overall hand OA, interphalangeal OA and thumb base OA.

METHODS

Multidisciplinary, international expert panels were convened. Patient vignettes were used to identify important features consistent with hand OA. A consensus-based decision analysis approach implemented using 1000minds software was applied to identify the most important features and their relative importance influencing the likelihood of symptoms being due to hand OA. Analyses were repeated for interphalangeal and thumb base OA. The reliability and validity of the proposed criteria sets were tested.

RESULTS

The experts agreed that the criteria sets should be applied in a population with pain, aching or stiffness in hand joint(s) not explained by another disease or acute injury. In this setting, five additional criteria were considered important: age, morning stiffness, radiographic osteophytes, radiographic joint space narrowing and concordance between symptoms and radiographic findings. The reliability and validity were very good.

CONCLUSION

Radiographic features were considered critical when determining whether a patient had symptoms due to hand OA. The consensus-based decision analysis approach in Phase 2 complemented the data-driven results from Phase 1, which will form the basis of the final classification criteria sets.

Ultrasound Elastography for Hand Soft Tissue Assessment

Over the past decade, ultrasound elastography has emerged as a new technique for measuring soft tissue properties. Real-time, noninvasive, and quantitative evaluations of tissue stiffness have improved and aid in the assessment of normal and pathological conditions. Specifically, its use has substantially increased in the evaluation of muscle, tendon, and ligament properties. In this review, the authors describe the principles of elastography and present different techniques including strain elastography and shear-wave elastography; discuss their applications for assessing soft tissues in the hand before, during, and postsurgeries; present the strengths and limitations of their measurement capabilities; and describe directions for future research.

Difference between bone age at the hand and elbow at the onset of puberty

In the pubertal period, bone age advances rapidly in conjunction with growth spurts. Precise bone-age assessments in this period are important, but results from the hand and elbow can be different. We aimed to compare the bone age between the hand and elbow around puberty onset and to elucidate the chronological age confirming puberty onset according to elbow-based bone age.A total of 211 peripubertal subjects (127 boys and 84 girls) who underwent hand and elbow radiographs within 2 months was enrolled. Two radiologists and a pediatric orthopedic surgeon assessed bone age. Hand bone age was graded using the Greulich-Pyle (GP) method, and elbow bone age was determined using the Sauvegrain method. The correlation of 2 methods was evaluated by Demining regression analysis, and the mean absolute difference (MAD) with chronological age was compared between pre-pubertal and pubertal subjects. Receiver-operating characteristic curve analysis was performed to determine the chronological age confirming puberty onset.There was a statistically significant difference in bone age revealed by the GP and Sauvegrain methods in the pubertal group. In the pubertal group, the MAD was 1.26 ± 0.90 years with the GP method and 0.61 ± 0.47 years with the Sauvegrain method in boys (P < .001), while in girls, the MAD was 0.84 ± 0.60 years and 0.53 ± 0.36 years with the same 2 methods (P = .033). The chronological age for confirming puberty onset using the elbow was 12.2 years in boys and 10.3 years in girls.The bone ages of hand and elbow were different at puberty, and the elbow was a more reliable location for bone-age assessment at puberty. Puberty onset according to elbow occurred slightly earlier than expected.

Use of deep learning methods for hand fracture detection from plain hand radiographs

BACKGROUND

Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients with hand trauma. Here, we aim to develop a computer-aided diagnosis (CAD) method to assist physicians in the diagnosis of hand fractures using deep learning methods.

METHODS

In this study, Convolutional Neural Networks (CNN) were used and the transfer learning method was applied. There were 275 fractured wrists, 257 fractured phalanx, and 270 normal hand radiographs in the raw dataset. CNN, a deep learning method, were used in this study. In order to increase the performance of the model, transfer learning was applied with the pre-trained VGG-16, GoogLeNet, and ResNet-50 networks.

RESULTS

The accuracy, sensitivity, specificity, and precision results in Group 1 (wrist fracture and normal hand) dataset were 93.3%, 96.8%, 90.3%, and 89.7%, respectively, with VGG-16, were 88.9%, 94.9%, 84.2%, and 82.4%, respectively, with Resnet-50, and were 88.1%, 90.6%, 85.9%, and 85.3%, respectively, with GoogLeNet. The accuracy, sensitivity, specificity, and precision results in Group 2 (phalanx fracture and normal hand) dataset were 84.0%, 84.1%, 83.8%, and 82.8%, respectively, with VGG-16, were 79.4%, 78.5%, 80.3%, and 79.7%, respectively, with Resnet-50, and were 81.7%, 81.3%, 82.1%, and 81.3%, respectively, with GoogLeNet.

CONCLUSION

We achieved promising results in this CAD method, which we developed by applying methods such as transfer learning, data augmentation, which are state-of-the-art practices in deep learning applications. This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.

OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.

Deep Learning Framework for Automatic Bone Age Assessment

Bone age Assessment or the skeletal age is a general clinical practice to detect endocrine and metabolic disarrangement in child development. The bone age indicates the level of structural and biological growth better than chronological age calculated from the birth date. The X-Ray of the wrist and hand is used in common to estimate the bone age of a person. The degree of agreement among the automated methods used to evaluate the X-rays is more than any other manual method. In this work, we propose a fully automated deep learning approach for bone age assessment. The dataset used is from the 2017 Pediatric Bone Age Challenge released by the Radiological Society of North America. Each X-Ray image in this dataset is an image of a left hand tagged with the age and gender of the patient. Transfer learning is employed by using pre-trained neural network architecture. InceptionV3 architecture is used in the present work, and the difference between the actual and predicted age obtained is 5.921 months.Clinical Relevance- This provides an AI-based computer assistance system as a supplement tool to help clinicians make bone age predictions.

Increased neural motor activation and functional reorganization in patients with idiopathic rapid eye movement sleep behavior disorder

INTRODUCTION

Altered brain activity and functional reorganization patterns during self-initiated movements have been reported in early pre-motor and motor stages of Parkinson's disease. The aim of this study was to investigate whether similar alterations can be observed in patients with idiopathic REM-sleep behavior disorder (RBD).

METHODS

13 polysomnography-confirmed male and right-handed RBD patients and 13 healthy controls underwent a bilateral hand-movement fMRI task including internally selected (INT) and externally-guided (EXT) movement conditions for each hand. We examined functional activity and connectivity differences between groups and task-conditions, structural differences using voxel-based morphometry, as well as associations between functional activity and clinical variables.

RESULTS

No group differences were observed in fMRI-task performance or in voxel-based morphometry. Both groups showed faster reaction times and exhibited greater neural activation when movements were internally selected compared to externally-guided tasks. Compared to controls, RBD patients displayed stronger activation in the dorsolateral prefrontal cortex and primary somatosensory cortex during INT-tasks, and in the right fronto-insular cortex during EXT-tasks performed with the non-dominant hand. Stronger activation in RBD patients was associated with cognitive and olfactory impairment. Connectivity analysis demonstrated overall less interregional coupling in patients compared to controls. In particular, patients showed reduced temporo-cerebellar, occipito-cerebellar and intra-cerebellar connectivity, but stronger connectivity in fronto-cerebellar and fronto-occipital pathways.

CONCLUSION

The observed stronger activation during hand-movement tasks and connectivity changes in RBD may reflect early compensatory and reorganization patterns in order to preserve motor functioning. Our findings may contribute to a better understanding and prognosis of prodromal stages of α-synucleinopathies.

Fatigue-sensitivity Comparison of sEMG and A-mode Ultrasound based Hand Gesture Recognition

Though physiological signal based human-machine interfaces (HMIs) have recently developed rapidly, their practical use is restricted by many real-world environmental factors, one of which is muscle fatigue. This paper explores the sensitivities between surface electromyography (sEMG) and A-mode ultrasound (AUS) sensing modalities subject to muscle fatigue in the context of hand gesture recognition tasks. Two metrics, mean classification accuracy (mCA) and decline rate (DR), are proposed to evaluate the accuracy and muscle fatigue sensitivity between sEMG and AUS based HMIs. Muscle fatigue inducing experiment was designed and eight subjects were recruited to participate in the experiment. The gesture recognition accuracies of sEMG and AUS under non-fatigue state and fatigue state are compared through Mahalanobis distance based classifier linear discriminant analysis (LDA). In addition, Mahalanobis distance based metrics, repeatability index (RI) and separability index(SI), are introduced to evaluate the changes in the feature distribution during muscle fatigue and reveal the cause of the fatigue sensitivity difference between sEMG and AUS signals. The experimental results demonstrate that the fatigue sensitivity of AUS signal is better than that of sEMG signal. Specifically, with the employment of the LDA classifier trained under non-fatigue state, the testing accuracy of the sEMG signal in the non-fatigue state is 94.96%, while reduce to 68.26% in the fatigue state. The testing accuracy of the AUS signal in the corresponding states is 99.68% and 91.24%. AUS signal attains a higher mCA and lower DR, indicating that it has advantages over sEMG signal in terms of both accuracy and muscle fatigue sensitivity. In addition, the RI and RI=SI analysis reveal that before and after muscle fatigue, the consistency of AUS feature distribution is better than that of sEMG. These research outcomes validate that AUS is more tolerant to feature migration caused by muscle fatigue than sEMG.

Spatio-temporal continuous gesture recognition under degraded environments: performance comparison between 3D integral imaging (InIm) and RGB-D sensors

In this paper, we introduce a deep learning-based spatio-temporal continuous human gesture recognition algorithm under degraded conditions using three-dimensional (3D) integral imaging. The proposed system is shown as an efficient continuous human gesture recognition system for degraded environments such as partial occlusion. In addition, we compare the performance between the 3D integral imaging-based sensing and RGB-D sensing for continuous gesture recognition under degraded environments. Captured 3D data serves as the input to a You Look Only Once (YOLOv2) neural network for hand detection. Then, a temporal segmentation algorithm is employed to segment the individual gestures from a continuous video sequence. Following segmentation, the output is fed to a convolutional neural network-based bidirectional long short-term memory network (CNN-BiLSTM) for gesture classification. Our experimental results suggest that the proposed deep learning-based spatio-temporal continuous human gesture recognition provides substantial improvement over both RGB-D sensing and conventional 2D imaging system. To the best of our knowledge, this is the first report of 3D integral imaging-based continuous human gesture recognition with deep learning and the first comparison between 3D integral imaging and RGB-D sensors for this task.

[Tendinopathies - Common Diagnoses in Hand Surgery]

Tendinopathies - Common Diagnoses in Hand Surgery Abstract. Tendinopathies are among the most frequent reasons for consulting a hand surgeon. The diagnosis can usually be made clinically. A supplementary ultrasound examination helps to visualize the pathology. Most of these diseases respond to non-surgical treatment. If surgical treatment is necessary, it can usually be performed as an outpatient procedure under local anesthesia. This article provides an overview of the most common tendinopathies of the hand and wrist, their diagnosis and treatment.

Natural History and Characteristics of Hand Exostoses in Multiple Hereditary Exostoses

PURPOSE

Multiple hereditary exostoses (MHEs) comprise a rare skeletal disorder. This study aimed to elucidate the natural history and characteristics of hand exostoses by focusing on their progression or regression and their association with shortening and angular deformation of the finger bones.

METHODS

Of 60 MHE patients who presented to our hospital between 2005 and 2019, 32 patients (62 hands) who underwent hand x-ray examinations were included in a study of initial presentation. Among them, 15 patients (30 hands) who underwent consecutive x-ray examinations before epiphyseal closure were included in a subsequent follow-up study (follow-up period, 6.9 years). We investigated the incidence and common location of hand involvement by exostoses during the initial examination study. We further investigated the progression or regression of hand exostoses and the influence of hand exostoses on longitudinal bone growth and the angular deformation of finger bones during the follow-up study.

RESULTS

In the initial study, we observed exostoses in 30 (60 hands) of 32 (62 hands) patients. The average number of exostoses per hand was 5.2. Exostoses developed more frequently in the middle and ring proximal phalanges and the fifth metacarpal. In the follow-up study, 3.1 exostoses per hand spontaneously regressed and 2.9 exostoses per hand occurred de novo with growth. Progression of exostoses was mainly observed by approximately age 15 years; thereafter, spontaneous regression became dominant. Regression of exostoses was frequently observed in metacarpals, whereas de novo exostoses frequently occurred in phalangeal bones, particularly in distal phalanges. Growth plate involvement by exostoses did not influence the longitudinal growth of finger bones, but it increased their angulation.

CONCLUSIONS

The hand is a common location of exostoses development for MHE patients. Although some exostoses in the hands regress with skeletal maturity, hand involvement by exostoses can result in angular deformity.

TYPE OF STUDY/LEVEL OF EVIDENCE

Diagnostic IV.

Evaluation of Hand Tendon Elastic Properties During Rehabilitation Through High-Frequency Ultrasound Shear Elastography

Tendon injuries lead to tendon stiffness, which impairs skeletal muscle movement. Most studies have focused on patellar or Achilles tendons by using ultrasound elastography. Only a few studies have measured the stiffness of hand tendons because their thickness is only 1-2 mm, rendering clinical ultrasound elastography unsuitable for mapping hand tendon stiffness. In this study, a high-frequency ultrasound shear elastography (HFUSE) system was proposed to map the shear wave velocity (SWV) of hand flexor tendons. A handheld vibration system that was coaxially mounted with an external vibrator on a high-frequency ultrasound (HFUS) array transducer allowed the operators to scan hand tendons freely. To quantify the performance of HFUSE, six parameters were comprehensively measured from homogeneous, two-sided, and three-sided gelatin phantom experiments: bias, precision, lateral resolution, contrast, contrast-to-noise ratio (CNR), and accuracy. HFUSE demonstrated an excellent resolution of [Formula: see text] to distinguish the local stiffness of thin phantom (thickness: 1.2 mm) without compromising bias, precision, contrast, CNR, and accuracy, which has been noted with previous systems. Human experiments involved four patients with hand tendon injuries who underwent ≥2 months of rehabilitation. Using HFUSE, two-dimensional SWV images of flexor tendons could be clearly mapped for healthy and injured tendons, respectively. The findings demonstrate that HFUSE can be a promising tool for evaluating the elastic properties of the injured hand tendon after surgery and during rehabilitation and thus help monitor progress.