Novel Technique for the Identification of Hip Implants Using Artificial Intelligence.

BACKGROUND: The anticipated growth of total hip arthroplasty will result in an increased need for revision total hip arthroplasty. Preoperative planning, including identifying current implants, is critical for successful revision surgery. Artificial intelligence (AI) is promising for aiding clinical decision-making, including hip implant identification. However, previous studies have limitations such as small datasets, dissimilar stem designs, limited scalability, and the need for AI expertise. To address these limitations, we developed a novel technique to generate large datasets, tested radiographically similar stems, and demonstrated scalability utilizing a no-code machine learning solution. METHODS: We trained, validated, and tested an automated machine learning-implemented convolutional neural network to classify 9 radiographically similar femoral implants with a metaphyseal-fitting wedge taper design. Our novel technique uses computed tomography-derived projections of a 3-dimensional scanned implant model superimposed within a computed tomography pelvis volume. We employed computer-aided design modeling and MATLAB to process and manipulate the images. This generated 27,020 images for training (22,957) and validation (4,063) sets. We obtained 786 test images from various sources. The performance of the model was evaluated by calculating sensitivity, specificity, and accuracy. RESULTS: Our machine learning model discriminated the 9 implant models with a mean accuracy of 97.4%, sensitivity of 88.4%, and specificity of 98.5%. CONCLUSIONS: Our novel hip implant detection technique accurately identified 9 radiographically similar implants. The method generates large datasets, is scalable, and can include historic or obscure implants. The no-code machine learning model demonstrates the feasibility of obtaining meaningful results without AI expertise, encouraging further research in this area.

Dopaminergic neurotoxins induce cell death by attenuating NF-κB-mediated regulation of TRPC1 expression and autophagy.

Alterations in Ca2+ homeostasis affect neuronal survival. However, the identity of Ca2+ channels and the mechanisms underlying neurotoxin-induced neuronal degeneration are not well understood. In this study, the dopaminergic neurotoxins 6-hydroxydopamine (6-OHDA) and 1-methyl-4-phenylpyridium ions (MPP+)/1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which mimic Parkinson's disease (PD), induced neuronal degeneration by decreasing store-mediated Ca2+ entry. The function of the transient receptor potential canonical (TRPC)-1 channel was decreased upon exposure to the neurotoxins, followed by a decrease in TRPC1 expression. Similar to neurotoxins, samples from patients with PD exhibited attenuated TRPC1 expression, which was accompanied by a decrease in autophagic markers and a subsequent increase in apoptosis markers. Furthermore, exposure to neurotoxins attenuated PKC phosphorylation, decreased expression of autophagic markers, and increased apoptosis in SHSY-5Y neuroblastoma cells, which was again dependent on TRPC1. Prolonged neurotoxin treatment attenuated the binding of NF-κB to the TRPC1 promoter, which resulted in a decrease in TRPC1 expression, thereby attenuating autophagy and activating cell death. Restoration of TRPC1 expression rescued the effects of the dopaminergic neurotoxins in neuroblastoma cells by increasing Ca2+ entry, restoring NF-κB activity, and promoting autophagy. Overall, these results suggest that dopaminergic neurotoxins initially decreased Ca2+ entry, which inhibited the binding of NF-κB to the TRPC1 promoter, thereby inhibiting TRPC1 expression and resulting in cell death by preventing autophagy.-Sukumaran, P., Sun, Y., Antonson, N., Singh, B. B. Dopaminergic neurotoxins induce cell death by attenuating NF-κB-mediated regulation of TRPC1 expression and autophagy.