Adaptive Electrical Signal Post-Processing in Optical Communication Systems

Improving bit error rates in optical communication systems is a difficult and important problem. The error correction must take place at high speed and be extremely accurate. We show the feasibility of using hardware implementable machine learning techniques. This may enable some error correction at the speed required

Posterior Tibial Slope in Anterior Cruciate Ligament Surgery: A Systematic Review

BACKGROUND: While the literature suggests a correlation between posterior tibial slope and sagittal stability of the knee, there is a lack of consensus relating to how to measure the slope, what a normal slope value would be, and which critical values should guide extra surgical treatment. We performed a systematic literature review looking at the posterior tibial slope and cruciate ligament surgery. Our aims were to define a gold standard measurement technique of posterior tibial slope, as well as determining its normal range and the important values for consideration of adjuncts during cruciate ligament surgery. METHODS: Electronic searches of MEDLINE (PubMed), CINAHL, Cochrane, Embase, ScienceDirect, and NICE in June 2020 were completed. Inclusion criteria were original studies in peer-reviewed English language journals. A quality assessment of included studies was completed using the Methodological Index for Non-Randomized Studies (MINORS) Criteria. RESULTS: Two-hundred and twenty-one papers were identified; following exclusions 34 papers were included for data collection. The mean MINORS score was 13.8 for non-comparative studies and 20.4 for comparative studies, both indicating fair to good quality studies. A large variation in the posterior tibial slope measurement technique was identified, resulting in a wide range of values reported. A significant variation in slope value also existed between different races, ages and genders. CONCLUSION: Cautiously, the authors suggest a normal range of 6-12º, using the proximal tibial axis at 5 and 15 cms below the joint. We suggest 12º as a cut-off value for slope-reducing osteotomy as an adjunct to revision ligament reconstruction.unknownThe article is available via Open Access. Click on the 'Additional link' above to access the full-text

The FAST Workstation Shows Construct Validity and Participant Endorsement

PURPOSE: To determine in what way the proposed simulation-based intervention (SBI) is an effective intervention for use in basic arthroscopic skills training. METHODS: Twenty candidates were recruited and grouped according to experience. Performance metrics included the time to activity completion, errors made, and Global Rating Scale score. Qualitative data were collected using a structured questionnaire. RESULTS: Performance on the SBI differed depending on previous arthroscopic training received. Performance on the simulator differed between groups to a statistically significant level regarding time to completion. A difference was also present between participants with no previous training and those with previous training when assessed using the Global Rating Scale. The SBI was deemed acceptable, user-friendly, and realistic. Participants practicing at the expert level believe that such an SBI would be beneficial in developing basic arthroscopic skills. CONCLUSIONS: The results of this study provide evidence that the use of an SBI consisting of a benchtop workstation, laptop viewing platform, 30° arthroscope, and defined performance metrics can detect differences in the level of arthroscopic experience. This format of SBI has been deemed acceptable and useful to the intended user, increasing the feasibility of introducing it into surgical training. CLINICAL RELEVANCE: This study adds to the existing body of evidence supporting the potential benefits of benchtop SBIs in arthroscopic skills training. Improved performance on such an SBI may be beneficial for the purpose of basic arthroscopic skills training, and we would support the inclusion of this system in surgical training programs such as those developed by the Arthroscopy Association of North America and American Board of Orthopaedic Surgery.The article is available via Open Access. Click on the 'Additional link' above to access the full-text.Unknow

Correcting Errors in Optical Data Transmission Using Neural Networks

“The original publication is available at www.springerlink.com”. Copyright Springer [Full text of this article is not available in the UHRA]Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.Peer reviewe

Explainable hierarchical clustering for patient subtyping and risk prediction

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK’s National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.</p

Adaptive electrical signal post-processing with varying representations in optical communication systems

“The original publication is available at www.springerlink.com”. Copyright Springer [Full text of this article is not available in the UHRA]Improving bit error rates in optical communication systems is a difficult and important problem. Error detection and correction must take place at: high speed, and be extremely accurate. Also, different communication channels have different characteristics, and those characteristics may change over time. We show the feasibility of using simple artificial neural Networks to address these problems; and examine tine effect of using different representations of signal waveforms on the accuracy of error correction. The results we have obtained lead us to the conclusion that a machine learning system based oil these principles cart improve on the performance of existing error correction Hardware at the speed required, whilst being able to adapt to suit; the characteristics of different communication channels.Peer reviewe

Dynamic Radiographs in Assessing Stability of Cervical Spine Fractures: A Multicentre Study

BACKGROUND: In the management of a trauma patient with cervical spine injury, the need for accurate diagnostic imaging is key to ensure correct management. Different classification systems have been developed including the Subaxial Injury Classification (SLIC) system and AO cervical spine fracture classification. Through a multicentre study, we have identified a group of cases where the use of CT alone to classify fractures by either SLIC or AO score may be deficient and the use of dynamic cervical spine radiographs could help identify instability. METHODS: Three level 1 trauma centers retrospectively reviewed patients with cervical spine injuries. Cervical spine radiographs (AP and lateral) were undertaken in collar, in all patients with suspected cervical spine injury within 2 weeks, followed by reanalysis of scoring systems. RESULTS: Eleven cases were identified in total, and 72% were male with a mean age of 65 years, with approximately 54% being older than 70 years. All patients reported their pain as severe using the Visual Analogue Scale scale. The predynamic radiograph mean SLIC score was 0.73, which is in contrast to the postdynamic radiograph mean SLIC score of 6. The statistical significance (P = 0.004) was found using the Wilcoxon signed-rank test. CONCLUSION: Supine imaging eliminates the gravitational loads normally exerted on the c-spine. The cases show assumed cervical stability based on CT, but dynamic c-spine radiographs subsequently demonstrated instability. Therefore, we suggest a combination of SLIC and AO classification using radiologic imaging to classify fracture and correlate clinical symptoms with persistent neck pain, which warrants a Miami-J collar and dynamic c-spine radiograph to assess stability with re-evaluation of scoring.Published version, accepted version (12 month embargo)Supports Open Acces