Novel cross LSTM for predicting the changes of complementary pelvic angles between standing and sitting

A Glossary of Terms in Artificial Intelligence for Healthcare

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution.

Sagittal balance in sitting and standing positions: A systematic review of radiographic measures

Background

Sagittal imbalance can be caused by various etiologies and is among the most important indicators of spinal deformity. Sagittal balance can be restored through surgical intervention based on several radiographic measures. The purpose of this study is to review the normal parameters in the sitting position, which are not well understood and could have significant implications for non-ambulatory patients.

Methods

A systematic review was performed adhering to PRISMA Guidelines. Using R-software, the weighted means and 95% confidence intervals of the radiographic findings were calculated using a random effect model and significance testing using unpaired t-tests.

Results

10 articles with a total of 1066 subjects reported radiographic measures of subjects with no spinal deformity in the sitting and standing position. In the healthy individual, standing sagittal vertical axis -16.8°was significantly less than sitting 28.4° (p < 0.0001), while standing lumbar lordosis 43.3°is significantly greater than sitting 21.3° (p < 0.0001). Thoracic kyphosis was not significantly different between the two groups (p = 0.368). Standing sacral slope 34.3° was significantly greater than sitting 19.5° (p < 0.0001) and standing pelvic tilt 14.0° was significantly less than sitting 33.9° (p < 0.0001).

Conclusions

There are key differences between standing and sitting postures, which could lead to undue stress on surgical implants and poor outcomes, especially for non-ambulatory populations. There is a need for more studies reporting sitting and standing radiographic measures in different postures and spinal conditions.

An artificial neural network model based on standing lateral radiographs for predicting sitting pelvic tilt in healthy adults

Background

Spinopelvic motion, the cornerstone of the sagittal balance of the human body, is pivotal in patient-specific total hip arthroplasty.

Purpose

This study aims to develop a novel model using back propagation neural network (BPNN) to predict pelvic changes when one sits down, based on standing lateral spinopelvic radiographs.

Methods

Young healthy volunteers were included in the study, 18 spinopelvic parameters were taken, such as pelvic incidence (PI) and so on. First, standing parameters correlated with sitting pelvic tilt (PT) and sacral slope (SS) were identified via Pearson correlation. Then, with these parameters as inputs and sitting PT and SS as outputs, the BPNN prediction network was established. Finally, the prediction results were evaluated by relative error (RE), prediction accuracy (PA), and normalized root mean squared error (NRMSE).

Results

The study included 145 volunteers of 23.1 ± 2.3 years old (M:F = 51:94). Pearson analysis revealed sitting PT was correlated with six standing measurements and sitting SS with five. The best BPNN model achieved 78.48% and 77.54% accuracy in predicting PT and SS, respectively; As for PI, a constant for pelvic morphology, it was 95.99%.

Discussion

In this study, the BPNN model yielded desirable accuracy in predicting sitting spinopelvic parameters, which provides new insights and tools for characterizing spinopelvic changes throughout the motion cycle.