This study aims to detect frontal pelvic radiograph femoral neck fracture using deep learning techniques. This retrospective study was conducted between January 2013 and January 2018. A total of 234 frontal pelvic X-ray images collected from 65 patients (32 males, 33 females; mean age 74.9 years; range, 33 to 89 years) were augmented to 2106 images to achieve a satisfactory dataset. A total of 1,341 images were fractured femoral necks while 765 were non-fractured ones. The proposed convolutional neural network (CNN) architecture contained five blocks, each containing a convolutional layer, batch normalization layer, rectified linear unit, and maximum pooling layer. After the last block, a dropout layer existed with a probability of 0.5. The last three layers of the architecture were a fully connected layer of two classes, a softmax layer and a classification layer that computes cross entropy loss. The training process was terminated after 50 epochs and an Adam Optimizer was used. Learning rate was dropped by a factor of 0.5 on every five epochs. To reduce overfitting, regularization term was added to the weights of the loss function. The training process was repeated for pixel sizes 50x50, 100x100, 200x200, and 400x400. The genetic algorithm (GA) approach was employed to optimize the hyperparameters of the CNN architecture and to minimize the error after testing the model created by the CNN architecture in the training phase. Performance in terms of sensitivity, specificity, accuracy, F1 score, and Cohen's kappa coefficient were evaluated using five- fold cross validation tests. Best performance was obtained when cropped images were rescaled to 50x50 pixels. The kappa metric showed more reliable classifier performance when 50x50 pixels image size was used to feed the CNN. The classifier performance was more reliable according to other image sizes. Sensitivity and specificity rates were computed to be 83% and 73%, respectively. With the inclusion of the GA, this rate increased by 1.6%. The detection rate of fractured bones was found to be 83%. A kappa coefficient of 55% was obtained, indicating an acceptable agreement. This experimental study utilized deep learning techniques in the detection of bone fractures in radiography. Although the dataset was unbalanced, the results can be considered promising. It was observed that use of smaller image size decreases computational cost and provides better results according to evaluation metrics.

Recently, the rate of the production and renewal of information makes it almost impossible to be updated. It is quite difficult to process and interpret large amounts of data by human beings. Unlimited memory capacities, learning abilities, artificial intelligence (AI) applications, and robotic surgery techniques cause orthopedic surgeons to be concerned about losing their jobs. The idea of AI, which was first introduced in 1956, has evolved over time by revealing deep learning and evolutionary plexus that can mimic the human neuron cell. Image processing is the leading improvement in developed algorithms. Theoretically, these algorithms appear to be quite successful in interpreting medical images and orthopedic decision support systems for preoperative evaluation. Robotic surgeons have emerged as significant competitors in carrying out the taken decisions. The first robotic applications of orthopedic surgery started in 1992 with the ROBODOC system. Applications started with hip arthroplasty continued with knee arthroplasty. Publications indicate that problems such as blood loss and infection caused by the long operation time in the early stages have been overcome in time with the help of learning systems. Comparative studies conducted with humans indicate that robots are better than humans in providing limb lengthening, patient satisfaction, and cost. As in all new technologies, the developments in both AI applications and robotics surgery indicate that technology is in favor in terms of cost/benefit analyses. Although studies indicate that new technologies are more successful than humans, the replacement of technology with experience and long-term results with traditional methods will not be observed in the near future.

Untreatable foot problems in diabetics may require lower extremity amputation, which has a high level of patient mortality. This high mortality rate is worse than most malignancies. The present study aimed to identify parameters that can be used to estimate survival in DM patients undergoing below-knee amputations for diabetic foot problems. A total of 470 patients (299 males, 171 females) with a mean age of 64.32 years who underwent below-knee amputation for diabetic foot problems between 2004 and 2014 were enrolled in the study. The length of time from the operation to time of death was recorded in days. Patient details were obtained, including age during surgery, BMI, oral antidiabetic and insulin usage, dialysis therapy history, lower extremity endovascular intervention, previous amputation at the same extremity, the need for stump revision surgery during follow-up, and above-knee amputation at the same site. Biochemical test results of pre-operative HbA1c, ESR, and levels of CRP, BUN, and creatinine were also obtained. A total of 333 patients (70.9%) died and 137 (29.1%) survived post-surgery. Survival rates were 90% in the first 7 days, 84% in the first 30 days, and 64% after the first year. Patient median life expectancy post-surgery was 930 ± 106 days. Hemodialysis treatment (p = 0.001), endovascular intervention (p = 0.04), sex (p = 0.004), age (p = 0.001), BUN level (p = 0.001), and duration of insulin use (p = 0.003) were shown to be effective predictors of mortality. Life expectancy is low (<3 years) in DM patients requiring below-knee amputations for untreatable foot problems. Survival could be predicted by duration of insulin use, age, sex, and renal insufficiency. Level IV, Therapeutic study.

Technological innovations in medicine are increasingly expected to replicate the diagnostic, decision-making, and procedural skills of experienced physicians. This review explores the evolution, current status, and limitations of artificial intelligence (AI) and robotic surgery in clinical practice. Radiology and pathology have led the way in digital transformation, enabling AI applications through standardized datasets and electronic health records. However, limitations such as algorithmic opacity, legal-ethical uncertainties, and fragmented digital infrastructures continue to hinder broader implementation. In robotic surgery, soft tissue procedures have not yet demonstrated significant advantages over conventional laparoscopy in terms of cost or operative efficiency. Orthopedic applications, particularly in arthroplasty, are promising but still lack long-term validation. Importantly, for any technology to create true value in healthcare, its benefits must clearly outweigh its costs. As healthcare systems face mounting pressure from aging populations and rising procedural demand, particularly for cholecystectomy, prostatectomy, and arthroplasty, the development of efficient and scalable technologies becomes inevitable. While neither AI nor robotic surgery has yet fulfilled its transformative promise, historical trends suggest that innovation will persist toward overcoming these barriers.