Three-dimensional corrective osteotomy for cubitus varus deformity using patient-matched instruments

Background

Various methods of two or three-dimensional (3D) corrective osteotomy for cubitus varus deformity have been reported. However, whether 3D correction of cubitus varus deformity is necessary is controversial because of technical difficulties and surgical complications. This study introduced 3D simulations and printing technology for corrective osteotomy against cubitus varus deformities. Moreover, recent studies on the application of these technologies were reviewed.

Methods

The amount of 3D deformity was calculated based on the difference in 3D shape between the affected side and the contralateral normal side. Patient-matched instruments were created to perform the actual surgery as simulated. Further, a 3D corrective osteotomy was performed using patient-matched instruments for cubitus varus deformity in pediatric and adolescent patients. The humerus-elbow-wrist angle, tilting angle, and elbow ranges of motion were evaluated.

Results

Humerus-elbow-wrist angle and tilting angle were corrected from -21° to 14° and from 30° to 43°, respectively, in the pediatric patient and from -18° to 10° and from 20° to 40°, respectively, in the adolescent patient. The elbow flexion and extension angles changed from 130° to 140° and from 20° to 10°, respectively, in the pediatric patient and from 120° to 130° and from 15° to 0°, respectively, in the adolescent patient.

Conclusion

The 3D computer simulations and the use of patient-matched instruments for cubitus varus deformity are reliable and can facilitate an accurate and safe correction. These technologies can simplify the complexity of 3D surgical procedures and contribute to the standardization of treatment for cubitus varus deformity.

Reproduction of forearm rotation dynamic using intensity-based biplane 2D-3D registration matching method

This study aimed to reproduce and analyse the in vivo dynamic rotational motion of the forearm and to clarify forearm motion involvement and the anatomical function of the interosseous membrane (IOM). The dynamic forearm rotational motion of the radius and ulna was analysed in vivo using a novel image-matching method based on fluoroscopic and computed tomography images for intensity-based biplane two-dimensional-three-dimensional registration. Twenty upper limbs from 10 healthy volunteers were included in this study. The mean range of forearm rotation was 150 ± 26° for dominant hands and 151 ± 18° for non-dominant hands, with no significant difference observed between the two. The radius was most proximal to the maximum pronation relative to the ulna, moved distally toward 60% of the rotation range from maximum pronation, and again proximally toward supination. The mean axial translation of the radius relative to the ulna during forearm rotation was 1.8 ± 0.8 and 1.8 ± 0.9 mm for dominant and non-dominant hands, respectively. The lengths of the IOM components, excluding the central band (CB), changed rotation. The transverse CB length was maximal at approximately 50% of the rotation range from maximum pronation. Summarily, this study describes a detailed method for evaluating in vivo dynamic forearm motion and provides valuable insights into forearm kinematics and IOM function.

Acceptable range of forearm deformity derived from relation to three-dimensional analysis and clinical impairments

This study aimed to investigate deformity patterns that cause clinical impairments and determine the acceptable range of deformity in the treatment of forearm diaphyseal fractures. A three-dimensional (3D) deformity analysis based on computed bone models was performed on 39 patients with malunited diaphyseal both-bone forearm fractures to investigate the 3D deformity patterns of the radius and ulna at the fracture location and the relationship between 3D deformity and clinical impairments. Clinical impairments were evaluated using forearm motion deficit. Cutoff values of forearm deformities were calculated by performing receiver operating characteristic analysis using the deformity angle and the limited forearm rotation range of motion (less than 50° of pronation or supination) resulting in activities of daily living (ADL) impairment as variables. The extension, varus, and pronation deformities most commonly occurred in the radius, whereas the extension deformity was commonly observed in the ulna. A positive correlation was observed between pronation deficit and extension deformity of the radius (R = 0.41) and between supination deficit and pronation deformity of the ulna (R = 0.44). In contrast, a negative correlation was observed between pronation deficit and pronation deformity of the radius (R = -0.44) and between pronation deficit and pronation deformity of the ulna (R = -0.51). To minimize ADL impairment, radial extension deformity should be <18.4°, radial rotation deformity <12.8°, and ulnar rotation deformity <16.6°. The deformities in the sagittal and axial planes of the radius and in the axial plane of the ulna were responsible for the limited forearm rotation.

Quantitative 3-D CT Demonstrates Distal Row Pronation and Translation and Radiolunate Arthritis in the SNAC Wrist

BACKGROUND

In scaphoid nonunion advanced collapse (SNAC) wrist arthritis, we analyzed the 3-dimensional (3-D) deformity patterns of carpal alignment secondary to scaphoid nonunion and quantified subchondral arthritis by investigating alterations in bone density.

METHODS

We constructed 3-D models of the carpal bones and radius from 51 patients with scaphoid nonunion (nonunion group) and 50 healthy controls (control group). We quantified the differences in 3-D geometric position of the distal carpal row relative to the distal radius in SNAC wrists versus controls. In addition, we assessed the bone density of anatomic regions of interest in the radiocarpal and capitolunate joints relative to the pisiform bone density to characterize degenerative changes in SNAC wrists.

RESULTS

The distal carpal row pronated by a difference of 14° (7.2° versus -6.7°; p < 0.001), deviated ulnarly by a difference of 19° (7.7° versus -11.2°; p < 0.001), shifted dorsally by a difference of 17% of the dorsovolar width of the distal radius (21.0% versus 4.4%; p < 0.001), shifted radially by a difference of 8% of the radioulnar width of the distal radius (13.2% versus 5.3%; p < 0.001), and migrated proximally by a difference of 12% of the lunate height (96.3% versus 108.8%; p < 0.001) in the nonunion group compared with the control group. Additionally, it was found that bone density was greater at the capitolunate joint (capitate head: 140.4% versus 123.7%; p < 0.001; distal lunate: 159.9% versus 146.3%; p < 0.001), the radial styloid (157.0% versus 136.3%; p < 0.001), and the radiolunate joint (proximal lunate: 134.8% versus 122.7%; p < 0.001; lunate fossa: 158.6% versus 148.1%; p = 0.005) in the nonunion group compared with the control group.

CONCLUSIONS

Scaphoid nonunion exhibited a unique deformity pattern and alteration in bone-density distributions. The distal carpal row not only shifted dorsally and migrated proximally but also pronated, deviated ulnarly, and shifted radially. Bone density was greater at the capitolunate joint, the radial styloid, and surprisingly, the radiolunate joint. Our findings give insight into the natural history and progression of arthritis of the SNAC wrist. Additionally, future studies may give insight into whether successful treatment of scaphoid nonunion arrests the progression of arthritis.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays

Background

Although the automatic diagnosis of fractures using artificial intelligence (AI) has recently been reported to be more accurate than those by orthopedics specialists, big data with at least 1000 images or more are required for deep learning of the convolutional neural network (CNN) to improve diagnostic accuracy. The aim of this study was to develop an AI system capable of diagnosing distal radius fractures with high accuracy even when learning with relatively small data by learning to use bi-planar X-rays images.

Methods

VGG16, a learned image recognition model, was used as the CNN. It was modified into a network with two output layers to identify the fractures in plain X-ray images. We augmented 369 plain X-ray anteroposterior images and 360 lateral images of distal radius fractures, as well as 129 anteroposterior images and 125 lateral images of normal wrists to conduct training and diagnostic tests. Similarly, diagnostic tests for fractures of the styloid process of the ulna were conducted using 189 plain X-ray anteroposterior images of fractures and 302 images of the normal styloid process. The distal radius fracture is determined by entering an anteroposterior image of the wrist for testing into the trained AI. If it identifies a fracture, it is diagnosed as the same. However, if the anteroposterior image is determined as normal, the lateral image of the same patient is entered. If a fracture is identified, the final diagnosis is fracture; if the lateral image is identified as normal, the final diagnosis is normal.

Results

The diagnostic accuracy of distal radius fractures and fractures of the styloid process of the ulna were 98.0 ± 1.6% and 91.1 ± 2.5%, respectively. The areas under the receiver operating characteristic curve were 0.991 {n = 540; 95% confidence interval (CI), 0.984–0.999} and 0.956 (n = 450; 95% CI 0.938–0.973).

Conclusions

Our method resulted in a good diagnostic rate, even when using a relatively small amount of data.

2D-3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

The purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

Utility of a 3-dimensionally printed color-coded bone model to visualize impinging osteophytes for arthroscopic débridement arthroplasty in elbow osteoarthritis

BACKGROUND

The identification and precise removal of bony impingement lesions during arthroscopic débridement arthroplasty for elbow osteoarthritis require a high level of experience and surgical skill. We have developed a new technique to identify impinging osteophytes on a computer display by simulating elbow motion using the multiple positions of 3-dimensional (3D) elbow models created from computed tomography data. Moreover, an actual color-coded 3D model indicating the impinging osteophytes was created with a 3D printer and was used as an intraoperative reference tool. This study aimed to verify the efficacy of these new technologies in arthroscopic débridement for elbow osteoarthritis.

METHODS

We retrospectively studied 16 patients treated with arthroscopic débridement for elbow osteoarthritis after a preoperative computer simulation. Patients who underwent surgery with only the preoperative simulation were assigned to group 1 (n = 8), whereas those on whom we operated using a color-coded 3D bone model created from the preoperative simulation were assigned to group 2 (n = 8). Elbow extension and flexion range of motion (ROM), the Mayo Elbow Performance Score (MEPS), and the severity of osteoarthritis were compared between the groups.

RESULTS

Although preoperative elbow flexion and MEPS values were not significantly different between the groups, preoperative extension was significantly more restricted in group 2 than in group 1 (P = .0131). Group 2 tended to include more severe cases according to the Hastings-Rettig classification (P = .0693). ROM and MEPS values were improved in all cases. No significant differences in postoperative ROM or MEPS values were observed between the groups. There were no significant differences in the improvement in ROM or MEPS values between the 2 groups.

CONCLUSIONS

The use of preoperative simulation and a color-coded bone model could help to achieve as good postoperative ROM and MEPS values for advanced elbow osteoarthritis as those for early and intermediate stages.

Cartilage wear patterns in severe osteoarthritis of the trapeziometacarpal joint: a quantitative analysis

OBJECTIVE

The present quantitative study aimed to assess the three-dimensional (3-D) cartilage wear patterns of the first metacarpal and trapezium in the advanced stage of osteoarthritis (OA) and compare cartilage measurements with radiographic severity.

DESIGN

Using 19 cadaveric trapeziometacarpal (TMC) joints, 3-D cartilage surface models of the first metacarpal and trapezium were created with a laser scanner, and 3-D bone surface model counterparts were similarly created after dissolving the cartilage. These two models were superimposed, and the interval distance on the articular surface as the cartilage thickness was measured. All measurements were obtained in categorized anatomic regions on the articular surface of the respective bone, and we analyzed the 3-D wear patterns on the entire cartilage surface. Furthermore, we compared measurements of cartilage thickness with radiographic OA severity according to the Eaton grading system using Pearson correlation coefficients (r).

RESULTS

In the first metacarpal, the cartilage thickness declined volarly (the mean cartilage thickness of the volar region was 0.32 ± 0.16 mm, whereas that of the dorsal region was 0.53 ± 0.18 mm). Conversely, the cartilage evenly degenerated throughout the articular surface of the trapezium. Measurements of the categorized regions where cartilage thinning was remarkable exhibited statistical correlations with radiographic staging (r = -0.48 to -0.72).

CONCLUSIONS

Our findings indicate that cartilage wear patterns differ between the first metacarpal and trapezium in the late stage of OA. There is a need for further studies on cartilage degeneration leading to symptomatic OA in the TMC joint.

Tether Creation Between the Second and Third Extensor Digitorum Communis for Third Extensor Tendon Subluxation at the Metacarpophalangeal Joint

We developed a substitution technique for a sagittal band reconstruction for subluxation of the third extensor tendon at the metacarpophalangeal joint. Through this procedure, we attempted to supply a tether between the second and third extensor digitorum communis (EDC) tendons. A half-slip of the second EDC was interlaced to the third EDC tendon at the level of the third metacarpal head with the metacarpophalangeal joint in full flexion position; this ensured that the interlace of the graft serves as a direct stabilizer against ulnar subluxation. A key advantage of our procedure is the strong and reliable interlace of the graft that does not require postoperative immobilization and allows unprotected motion protocol.