Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages.

OBJECTIVE: This study aimed to evaluate a new deep-learning model for diagnosing avascular necrosis of the femoral head (AVNFH) by analyzing pelvic anteroposterior digital radiography. METHODS: The study sample included 1167 hips. The radiographs were independently classified into 6 stages by a radiologist using their simultaneous MRIs. After that, the radiographs were given to train and test the deep learning models of the project including SVM and ANFIS layer using the Python programming language and TensorFlow library. In the last step, the test set of hip radiographs was provided to two independent radiologists with different work experiences to compare their diagnosis performance to the deep learning models' performance using the F1 score and Mcnemar test analysis. RESULTS: The performance of SVM for AVNFH detection (AUC = 82.88%) was slightly higher than less experienced radiologists (79.68%) and slightly lower than experienced radiologists (88.4%) without reaching significance (p-value > 0.05). Evaluation of the performance of SVM for pre-collapse AVNFH detection with an AUC of 73.58% showed significantly higher performance than less experienced radiologists (AUC = 60.70%, p-value < 0.001). On the other hand, no significant difference is noted between experienced radiologists and SVM for pre-collapse detection. ANFIS algorithm for AVNFH detection with an AUC of 86.60% showed significantly higher performance than less experienced radiologists (AUC = 79.68%, p-value = 0.04). Although reaching less performance compared to experienced radiologists statistically not significant (AUC = 88.40%, p-value = 0.20). CONCLUSIONS: Our study has shed light on the remarkable capabilities of SVM and ANFIS as diagnostic tools for AVNFH detection in radiography. Their ability to achieve high accuracy with remarkable efficiency makes them promising candidates for early detection and intervention, ultimately contributing to improved patient outcomes.

The results of biplanar distal femoral osteotomy; a case series study.

BACKGROUND: Distal femur wedge osteotomies for varus or valgus alignment of the lower extremity could be done in either uniplanar or biplanar fashion.Union time and stability of the osteotomy site has been considered important in this anatomic region. In this study, clinical and radiographic findings of biplane distal femur osteotomy were reported. METHODS: Clinical, functional, and radiological findings of eight patients (10 knees) underwent biplane distal femur osteotomy were evaluated. Visual analogue score (VAS) and Lysholm-Tegner knee score were used for the assessment of pain and function before and three months after surgery. RESULTS: In this study, eight patients were included. All patients were female. The mean age was 28±6.3. The mean pre-operative mechanical angle was 8.7±2.2° and the post-operative angle was 1.4±0.53° in patients with valgus alignment whileit was 7.0±1.0°preoperatively and 0.66±1.2° postoperatively in patients with varus alignment. The mean lateral distal femoral angle (LDFA) was 85±8.0° before surgery and was 88±1.3° after surgery. According to Lysholm-Tegner knee score, in the post-operative visit, six knees were good and four were excellent. The mean union time was 9.2±2.3 weeks. CONCLUSIONS: Biplane distal femur osteotomy is a reliable technique that creates larger surfaces and more stability at the osteotomy site with further rapid union.