PURPOSE: Inaccurate femoral canal shaping can result in post-operative complications in hip arthroplasty. We addressed the amount of broach rotation during shaping of the femoral canal and compared it with respect to newly designed broaches which were modified to minimize the rotation. MATERIALS AND METHOD: we designed the broaches that had canal guide which restricts the broach motion such that it is always aligned with the femoral axis while the broach machines the metaphyseal bone. Conventional broaches and the modified broach applied to 5 pair of fresh-frozen cadaver femurs and its spatial motion was measured with motion tracker. Rotations in coronal, saggital and frontal plane during the final 10 mm of broach advance were measured. RESULTS: 2.4..of axial rotation was occurred during final 10 mm advance of broach in the conventional method, which was the largest component of the rotation. Rotation of the broach during machining was decreased to 37% (p=0.075) and 25% (p=0.042) in the sagittal plane and coronal plane, respectively, by proposed method. CONCLUSION: The canal guide in the proposed method significantly reduced the rotation of the broach without any extra incision or measurement devices, resulting in increased accuracy in the femoral canal shaping.
Arthroplasty ; Axis, Cervical Vertebra ; Cadaver ; Femur ; Hip

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.
Artificial Intelligence* ; Capsule Endoscopy* ; Classification ; Diagnosis ; Endoscopy ; Specialization

Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed.
Capsule Endoscopes ; Capsule Endoscopy ; Cooperative Behavior ; Dataset ; Methods