Osteosarcoma of the spine accounts for 1 to 3 percent of osteosarcoma. When this afflication of the spine occurs, it is often fraught with diagnostic dilemmas and treatment challenges.

OBJECTIVE: This manuscript reports on the cases reported over a 15- year period from the UP Musculoskeletal Tumor Unit. Only 2 of the 3 cases were available for review.

RESULTS: Both patients were initially misdiagnosed. This delay in diagnosis led to a delay in instituting appropriate chemotheraphy inevitable causing poor outcomes of treatment.

CONCLUSION: This report highlights the dilemma of treating a difficult site. Spinal osteosarcoma presents with numerous diagnostics and management obstacles. Unless clinician awareness is improved and these treatment options become available to patients, prognosis will not improve.

Human ; Male ; Female ; Adult ; Osteosarcoma ; Bone Neoplasms ; Prognosis ; Spine ; Treatment Outcome

BACKGROUND: Hip fractures are commonly missed on the first radiograph in up to 30% of patients. The delay in diagnosis leads to significant gaps in management and consequent morbidities. Thus, a computer-aided hip fracture recognition through the Artificial Neural Network deep learning model, which allows the program to learn and gain experience with more images processed, has been created. The study aimed to determine the accuracy and sensitivity of the artificial neural network model in detecting fractures of the hip and explored the feasibility of its use as a diagnostic screening tool. MATERIALS AND METHODS: A sample size of 45 participants/samples per treatment group was computed using a confidence level of 90%, and prevalence of 0.05 for a pilot study. The program was tested by processing digital pictures of radiographs of patients with known hip fractures that included femoral neck, intertrochanteric, subtrochanteric, and proximal femur fractures taken from the database of adult patients, who have undergone surgery for a hip fracture at the Philippine General Hospital from 2016-17. The 90 (45 fractured, 45 normal) manually selected proximal femur images were run on 10 models. The models were based on AlexNet and VGG-16, which are the representative convoluted neural networks designed for image analysis. RESULTS AND CONCLUSION: The program had an accuracy of 70%, specificity of 42.2% and sensitivity of 97.8%. This study is proof of concept that a deep learning model fracture detection software shows potential in hip fracture detection. Further training is necessary to make this promising innovation clinically useful.
Artificial Intelligence ; Machine Learning ; Hip Fractures