ACR Appropriateness Criteria® Chronic Hand and Wrist Pain: 2023 Update.

Chronic hand and wrist pain is a common presenting complaint. The intricate anatomy results in a variety of pain generators-multiple bones, articular cartilage, intrinsic ligaments, triangular fibrocartilage complex, joint capsules and synovium, tendons and tendon sheaths, muscles, and nerves-in a compact space. The need for imaging and the choice of the appropriate imaging modality are best determined by the patient's presentation, physical examination, and the clinician's working differential diagnosis. Radiography is usually appropriate as the initial imaging study in the evaluation of chronic hand or wrist pain. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

DCES-PA: Deformation-controllable elastic shape model for 3D bone proliferation analysis using hand HR-pQCT images.

Bone proliferation is an important pathological feature of inflammatory rheumatic diseases. Although recent advance in high-resolution peripheral quantitative computed tomography (HR-pQCT) enables physicians to study microarchitectures, physicians' annotation of proliferation suffers from slice inconsistency and subjective variations. Also, there are only few effective automatic or semi-automatic tools for proliferation detection. In this study, by integrating pathological knowledge of proliferation formation with the advancement of statistical shape analysis theory, we present an unsupervised method, named Deformation-Controllable Elastic Shape model, for 3D bone Proliferation Analysis (DCES-PA). Unlike previous shape analysis methods that directly regularize the smoothness of the displacement field, DCES-PA regularizes the first and second-order derivative of the displacement field and decomposes these vector fields according to different deformations. For the first-order elastic metric, DCES-PA orthogonally decomposes the first-order derivative of the displacement field by shearing, scaling and bending deformation, and then penalize deformations triggering proliferation formation. For the second-order elastic metric, DCES-PA encodes both intrinsic and extrinsic surface curvatures into the second-order derivative of the displacement field to control the generation of high-curvature regions. By integrating the elastic shape metric with the varifold distances, DCES-PA achieves correspondence-free shape analysis. Extensive experiments on both simulated and real clinical datasets demonstrate that DCES-PA not only shows an improved accuracy than other state-of-the-art shape-based methods applied to proliferation analysis but also produces highly sensitive proliferation annotations to assist physicians in proliferation analysis.

Fluoroscopy and Radiographs for Detecting Retained Surgical Needles in the Hand.

When a surgical needle is lost, the protocol is to explore the surgical field and to obtain a plain radiograph if the needle cannot be located. The size of the needle that can be detected with imaging is debated. Plain-film radiographs, C-arm, and mini C-arm fluoroscopy imaging was obtained of a cadaveric hand with retained needle of varying lengths (suture sizes 4-0 - 10-0). The authors performed analyses to determine the sensitivity and specificity of the imaging modalities. There were no differences in diagnostic area under the receiver operating characteristic curve between the three modalities. For plain film, optimal cutoff for needle size was 5.2 mm (sensitivity 0.87, specificity 0.75), for C-arm 6.8 mm (sensitivity 0.84, specificity 0.87), and for mini C-arm 5.9 mm (sensitivity 0.82, specificity 0.86). In the hand, the use of C-arm fluoroscopy is as sensitive as plain-film radiography at detecting retained needles greater than 5.9 mm. (Journal of Surgical Orthopaedic Advances 33(1):026-028, 2024).

Vertical mandibular asymmetry and hand measurements in patients in growth: cross-sectional study.

Objectives: The aim of this study was to determine the relationship between the vertical mandibular ramus asymmetry and the hand measurements asymmetry in growing patients. Material and Methods: Panoramic radiographs and comparative radiographs of the right and left hands of 40 patients (14 males and 26 females) between 6 and 16 years old (mean age of 11.35±1.99 years) were evaluated. The total height of the mandibular ramus was measured, and the asymmetry index was calculated. The lengths of the proximal, middle and distal phalanges and the metacarpals of the five digits of both hands were measured and the absolute differences R-L were calculated for each one. Results: A statistically significant association between the presence of vertical mandibular ramus asymmetry and the presence of phalanges asymmetry FPII (p=0.016), FPIII (p=0.016), FPIV (p=0.033), FMIII (p=0.031) and FMIV (p=0.016) was observed. Conclusions: Mandibular asymmetry appears not to be isolated but also present in other body districts such as the phalanx bones. The total vertical asymmetry of the mandibular ramus showed an association with the asymmetry of the lengths of the FPIII, FMIII, FPIV, FMIV and FPII in growing individuals, with association between the side of mandibular asymmetry and the side of the phalanx's asymmetry. The asymmetry of this phalanges increases with increasing index of vertical mandibular ramus asymmetry.

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.

Ultrasound identification of hand and wrist anatomical structures by hand surgeons new to ultrasonographic techniques.

PURPOSE: Ultrasound is becoming an essential tool for hand surgeons, but most of them are trained on the job, without any diploma or dedicated training. The aim of this study was to assess the ability of hand surgeons new to ultrasound to identify hand and wrist anatomical structures. METHODS: A monocentric study was conducted from January 2022 to April 2022. Ten residents and five attending hand surgeons, ultrasound novices, were involved in this study. The participants underwent two tests, wherein they were required to identify 17 anatomical structures using ultrasound, on the same subject. The second test was similar and carried out 2 to 6 weeks later by all participants. The number of structures successfully identified and if it was the case, the detection time per structure, were recorded. The correlations between participants age, years of surgical experience, surgical background (orthopedic or plastic) and the ability to perform immediately during the first test or to progress between the two tests were also assessed. RESULTS: The average number of structures identified during the first test (T1) was 14.1+/-2.1 (82.9%), versus 16.2+/-0.8 (95.3%) structures during the second test (T2) (p = 0.001). The mean detection time per structure was 53.4 +/- 18.9 s during T1 versus 27.7 +/- 7.2 s during T2 (p < 0.0001). A moderate negative correlation between the progression in the number of anatomical structures identified between the two tests and the years of surgical experience (ρ=-0.56; p = 0.029) was found. The other parameters were neither correlated with the ability to perform at the first test nor with the progression between the two tests. CONCLUSION: Hand surgeons new to ultrasound are most of the time able to identify hand and wrist anatomical structures. Comparison of their first and second tests showed significant potential for improvement in anatomical structure identification and detection time of those, especially in surgeons with limited surgical experience.

3D MODEL of an anatomically inert human hand: feasibility study.

OBJECTIVES: Surgery for congenital malformation of the hand is complex and protocols are not available. Simulation could help optimize results. The objective of the present study was to design, produce and assess a 3D-printed anatomical support, to improve success in rare and complex surgeries of the hand. MATERIAL AND METHODS: We acquired MRI imaging of the right hand of a 30 year-old subject, then analyzed and split the various skin layers for segmentation. Thus we created the prototype of a healthy hand, using 3D multi-material and silicone printing devices, and drew up a printing protocol suitable for all patients. We printed a base comprising bones, muscles and tendons, with a multi-material 3D printer, then used a 3D silicone printer for skin and subcutaneous fatty cell tissues in a glove-like shape. To evaluate the characteristics of the prototype, we performed a series of dissections on the synthetic hand and on a cadaveric hand in the anatomy lab, comparing realism, ease of handling and the final result of the two supports, and evaluated their respective advantages in surgical and training contexts. A grading form was given to each surgeon to establish a global score. RESULTS: This evaluation highlighted the positive and negative features of the model. The model avoided intrinsic problems of cadavers, such as muscle rigidity or tissue fragility and atrophy, and enables the anatomy of a specific patient to be rigorously respected. On the other hand, vascular and nervous networks, with their potential anatomical variants, are lacking. This preliminary phase highlighted the advantages and inconveniences of the prototype, to optimize the design and printing of future models. It is an indispensable prerequisite before performing studies in eligible pediatric patients with congenital hand malformation. CONCLUSION: The validation of 3D-printed anatomical model of a human hand opens a large field of applications in the area of preoperative surgical planning. The postoperative esthetic and functional benefit of such pre-intervention supports in complex surgery needs assessing.

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.

Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning.

PURPOSE: Vascular distribution is important information for diagnosing diseases and supporting surgery. Photoacoustic imaging is a technology that can image blood vessels noninvasively and with high resolution. In photoacoustic imaging, a hemispherical array sensor is especially suitable for measuring blood vessels running in various directions. However, as a hemispherical array sensor, a sparse array sensor is often used due to technical and cost issues, which causes artifacts in photoacoustic images. Therefore, in this study, we reduce these artifacts using deep learning technology to generate signals of virtual dense array sensors. METHODS: Generating 2D virtual array sensor signals using a 3D convolutional neural network (CNN) requires huge computational costs and is impractical. Therefore, we installed virtual sensors between the real sensors along the spiral pattern in three different directions and used a 2D CNN to generate signals of the virtual sensors in each direction. Then we reconstructed a photoacoustic image using the signals from both the real sensors and the virtual sensors. RESULTS: We evaluated the proposed method using simulation data and human palm measurement data. We found that these artifacts were significantly reduced in the images reconstructed using the proposed method, while the artifacts were strong in the images obtained only from the real sensor signals. CONCLUSION: Using the proposed method, we were able to significantly reduce artifacts, and as a result, it became possible to recognize deep blood vessels. In addition, the processing time of the proposed method was sufficiently applicable to clinical measurement.

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).

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.

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%.

Applications of ultrasound elastography to hand and upper limb disorders.

Ultrasound elastography is a recently developed method for accurate measurement of soft tissue stiffness in addition to the clinician's subjective evaluation. The present review briefly describes the ultrasound elastography techniques and outlines clinical applications for tendon, muscle, nerve, skin and other soft tissues of the hand and upper limb. Strain elastography provides a qualitative evaluation of the stiffness, and shear-wave elastography generates quantitative elastograms superimposed on a B-mode image. The stiffness in degenerative tendinopathy and/or tendon injury was significantly lower than in a normal tendon in several studies. Elastography is also a reliable method to evaluate functional muscle activity, compared to conventional surface electromyography. The median nerve is consistently stiffer in patients with carpal tunnel syndrome than in healthy subjects, on whatever ultrasound elastography technique. Elastography distinguishes normal skin from scars and can be used to evaluate scar severity and treatment. Elastography has huge clinical applications in musculoskeletal tissues. Continued development of systems and increased training of clinicians will expand our knowledge of elastography and its clinical applications in the future.

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.

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.

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.

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.

Whole-hand and regional bone mineral density involvement in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by symmetric polyarthritis that can lead to joint deformity, disability, and osteoporosis. We aimed to evaluate whole hand and regional BMD in RA patients compared to controls. In addition, we evaluated the BMD of dominant versus non-dominant hands in healthy subjects. We included adult female and male RA patients and control subjects matched by age, sex, and BMI. BMD (g/cm2) was measured by DXA in lumbar spine (LS), whole hand, and three regions of interest: carpus, metacarpal bones, and phalanges. Results: 44 control subjects (49.5±11.8 y) and 60 with RA (52.7±12.7 y) were included. Significant lower BMD in RA patients was found in LS (-8.7%), dominant whole hand (-9.5%), carpus, metacarpal bones, and phalanges, and non-dominant whole hand (-8.7%), metacarpal bones, and phalanges compared to controls. A significant positive correlation was found between LS and whole-hand BMD (dominant r=.63, non-dominant r=.67). Finally, the whole hand, metacarpal bones, and carpus BMD measurements were significantly higher in the dominant hand compared to the non-dominant hand without differences in the phalangeal ROI. In conclusion, hand BMD was significantly lower in RA patients compared to control subjects and there was a significant correlation with LS BMD. We demonstrated that BMD measurements of the whole-hand, and different ROI (carpus, metacarpal bones, and phalanges) by DXA would be an easily reproducible technique to evaluate bone loss. In addition, the whole hand, metacarpal bones and carpus BMD measurements were significantly higher in the dominant hand compared to the non-dominant hand without differences in the phalanges.

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.