Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.

PURPOSE: Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosis and surgical planning for ACL injuries. METHODS: Images and demographic information from 120 patients who underwent ACL reconstruction surgery were used for this study. An AI-driven model was developed to measure the posterior lateral tibial slope using the YOLOv8 algorithm. The accuracy of the lateral tibial slope, medial tibial slope and tibial longitudinal axis measurements was assessed, and the results reached high levels of reliability. This study employed machine learning and AI techniques to provide objective, consistent and efficient measurements of tibial slopes on MR images. RESULTS: Three distinct models were developed to derive AI-based measurements. The study results revealed a substantial correlation between the measurements obtained from the AI models and those obtained by the orthopaedic surgeon across three parameters: lateral tibial slope, medial tibial slope and tibial longitudinal axis. Specifically, the Pearson correlation coefficients were 0.673, 0.850 and 0.839, respectively. The Spearman rank correlation coefficients were 0.736, 0.861 and 0.738, respectively. Additionally, the interclass correlation coefficients were 0.63, 0.84 and 0.84, respectively. CONCLUSION: This study establishes that the deep learning-based method for measuring posterior tibial slopes strongly correlates with the evaluations of expert orthopaedic surgeons. The time efficiency and consistency of this technique suggest its utility in clinical practice, promising to enhance workflow, risk assessment and the customization of patient treatment plans. LEVEL OF EVIDENCE: Level III, cross-sectional diagnostic study.

Arthroscopic Pan-Capsular and Transverse Humeral Ligament Release with Biceps Tenodesis for Patients with Refractory Frozen Shoulder.

Arthroscopic capsular release allows direct visualization and release of inflamed tissues in refractory frozen shoulder. The reticular neural network in the long head of the biceps tendon (LHBT) and nerve endings of the transverse humeral ligament (THL) might be responsible for shoulder pain. We hypothesized that patients with painful refractory frozen shoulder benefited from pan-capsular release, THL release, and LHBT tenodesis. The LHBT tenodesis decreased the possibility of LHBT instability. The balance of the shoulder joint was maintained after such extensive release. From October 2013 to June 2019, patients with painful refractory frozen shoulder were enrolled consecutively at the same institute. All patients received arthroscopic pan-capsular, THL release, and suprapectoral LHBT tenodesis with a minimum of 2-year follow-up. Preoperative and postoperative shoulder range of motion (ROM), pain visual analog scale (PVAS), subjective shoulder value (SSV), constant score, LHBT score, acromio-humeral distance (AHD), and critical shoulder angle (CSA) were recorded. In total, 35 patients with an average age of 53.1 ± 9 years were enrolled. The average follow-up period was 24 ± 1.5 months. Forward elevation improved from 105.1° ± 17° to 147° ± 12° (p < 0.001), external rotation improved from 24.1° ± 13.3° to 50.9° ± 9.7° (p < 0.001), and internal rotation improved from L3 to T9 (p < 0.001), respectively, at final follow-up. PVAS improved from 7.3 ± 1.1 to 1.8 ± 0.6 (p < 0.001), constant score from 23.4 ± 11 to 80.7 ± 5.2 (p < 0.001), and SSV from 27.7 ± 10.5 to 77.4 ± 3.8, respectively, at follow-up. No differences were found in AHD and CSA after surgery (p = 0.316, and p = 0.895, respectively). Patients with painful refractory frozen shoulder benefited from pan-capsular and THL release. A radiographically balanced shoulder joint was maintained even after such extensive release.

Rectifying Resistive Memory Devices as Dynamic Complementary Artificial Synapses.

Brain inspired computing is a pioneering computational method gaining momentum in recent years. Within this scheme, artificial neural networks are implemented using two main approaches: software algorithms and designated hardware architectures. However, while software implementations show remarkable results (at high-energy costs), hardware based ones, specifically resistive random access memory (RRAM) arrays that consume little power and hold a potential for enormous densities, are somewhat lagging. One of the reasons may be related to the limited excitatory operation mode of RRAMs in these arrays as adjustable passive elements. An interesting type of RRAM was demonstrated recently for having alternating (dynamic switching) current rectification properties that may be used for complementary operation much like CMOS transistors. Such artificial synaptic devices may be switched dynamically between excitatory and inhibitory modes to allow doubling of the array density and significantly reducing the peripheral circuit complexity.

Nanoscale Conductive Filament with Alternating Rectification as an Artificial Synapse Building Block.

A popular approach for resistive memory (RRAM)-based hardware implementation of neural networks utilizes one (or two) device that functions as an analog synapse in a crossbar structure of perpendicular pre- and postsynaptic neurons. An ideal fully automated, large-scale artificial neural network, which matches a biologic counterpart (in terms of density and energy consumption), thus requires nanosized, extremely low power devices with a wide dynamic range and multilevel functionality. Unfortunately the trade-off between these traits proves to be a serious obstacle in the realization of brain-inspired computing platforms yet to be overcome. This study demonstrates an alternative manner for the implementation of artificial synapses in which the local stoichiometry of metal oxide materials is delicately manipulated to form a single nanoscale conductive filament that may be used as a synaptic gap building block in an equivalent manner to the functionality of a single connexon (a signaling pore between synapses) with dynamic rectification direction. The structure, of a few nanometers in size, is based on the formation of defect states and shows current rectification properties that can be consecutively flipped to a forward or reverse direction to create either an excitatory or inhibitory (positive or negative) weight parameter. Alternatively, a plurality of these artificial connexons may be used to create a synthetic rectifying synaptic gap junction. In addition, the junction plasticity may be altered in a differential digital scheme (opposed to conventional analog RRAM conductivity manipulation) by changing the ratio of forward to reverse rectifying connexons.

A spherical harmonic transform spectral analysis of a localized surface plasmon on a gold nano shell.

We perform a study of the localized surface plasmon (LSP) modes of a gold nano shell having a silica core by means of discrete dipole approximation (DDA) and spherical harmonics transform for selected wavelengths. We demonstrate an efficient solution for the near and intermediate field terms by the dyadic Green function approach and determine the optical extinction efficiency by the far field term. Using this approach, we combine the advantages of a spectral analysis along with a DDA flexibility to solve an arbitrary shaped model and demonstrate the LSP dominant mode wavelength dependency. Our approach provides a metric which may be used to quantify the effects of minor changes in the model structure, or the external dielectric environment, in optical experiments.

Plasmon resonance based analysis of a single protein conjugated Au nanoshell.

The authors perform a numerical calculation of a gold nanoshell, having either a silica core or a hollow one, interacting with incident electromagnetic radiation and aggregated with a single protein on its surface, by using the discrete dipole approximation. The protein model accounts for a tertiary structure that may contain internal cavities, while the entire structure is placed either in vacuum or a uniform host medium. The authors further analyze the near field pattern by a spherical harmonic transform. Our results identify the interactions that account for the observed extinction peak and wavelength shift in related optical experiments. The theoretical basis provided in this work may be used not only to identify a single protein conjugated nanoparticle, but also to determine the cavity content of the protein by its refractive index properties, determined from optical measurements.