X-Ray Film under Artificial Intelligence Algorithm in the Evaluation for Nursing Effect of Gamma Nail Internal Fixation in Elderly Patients with Intertrochanteric Fracture of Femur

• Published in: Computational and mathematical methods in medicine Vol. 2021; pp. 1 - 11
• Main Authors: Wang, Yudi, Wang, Xin, Jin, Lei, Wei, Xuemei
• Format: Journal Article
• Language: English
• Published: United States Hindawi 30.11.2021
• Subjects: Aged; Aged, 80 and over; Algorithms; Artificial Intelligence; Bone Nails; Computational Biology; Female; Femoral Fractures - diagnostic imaging; Femoral Fractures - nursing; Femoral Fractures - surgery; Fracture Fixation, Internal - instrumentation; Fracture Fixation, Internal - nursing; Hip Fractures - diagnostic imaging; Hip Fractures - nursing; Hip Fractures - surgery; Humans; Male; Middle Aged; Postoperative Complications - etiology; Prognosis; Radiographic Image Interpretation, Computer-Assisted - methods; Radiographic Image Interpretation, Computer-Assisted - statistics & numerical data
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2021/2562575

The aim of this work was to explore the effects of Gamma nail internal fixation for intertrochanteric fracture of femur by X-ray film classification and recognition method based on artificial intelligence algorithm. The study subjects were 100 elderly patients with intertrochanteric fracture of femur admitted to hospital. The cases were diagnosed as elderly (over 60 years old) femoral intertrochanteric fractures by X-ray or CT. They were divided into two groups, with 50 persons in each group: one group used the X-ray film evaluation image guidance based on the artificial intelligence algorithm (research group), and the other group did not use algorithmic guidance (control group). The results showed that the segmentation effect of the proposed algorithm was similar to the gold standard segmentation result, indicating that the algorithm was effective and feasible in the segmentation of fractures and bones. The global level set algorithm was set as the control. The ultimate measurement accuracy (UMA) value of the algorithm group was (1.77±0.22), and the UMA value of the global level set algorithm group was (3.42±0.36), indicating that the image processed by the algorithm group had obvious numerical effect, high accuracy, and good retention of details. The operation time, intraoperative blood loss, incision length, hospital stay, weight-bearing time, and fracture healing time of the two groups were all better than those of the control group. One month after surgery, the Harris score of the algorithm group was 67, and that of the control group was 51, with a 16-point difference between the two groups (p<0.05). The patient had less pain and fast recovery speed, indicating that it was a good way to treat elderly intertrochanteric fractures with the nursing effect of X-ray Gamma nail internal fixation based on an artificial intelligence algorithm. The artificial intelligence algorithm not only can be applied to the Gamma nail internal fixation of elderly patients with intertrochanteric fractures but also can be applied to the X-ray image processing of other fractures and other surgical methods to provide effective treatment for fracture patients.