Validation of a Mathematical Procedure for the Cobb Angle Assessment Based on Photogrammetry

Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, hypo/hyperlordosis, or hypo/hyperkyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person’s diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review the recent works on spinal deformity assessment for scoliosis diagnosis utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022 using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.

Photogrammetry Applied to Neurosurgery: A Literature Review

Photogrammetry refers to the process of creating 3D models and taking measurements through the use of photographs. Photogrammetry has many applications in neurosurgery, such as creating 3D anatomical models and diagnosing and evaluating head shape and posture deformities. This review aims to summarize the uses of the technique in the neurosurgical practice and showcase the systems and software required for its implementation. A literature review was done in the online database PubMed. Papers were searched using the keywords "photogrammetry", "neurosurgery", "neuroanatomy", "craniosynostosis" and "scoliosis". The identified articles were later put through primary (abstracts and titles) and secondary (full text) screening for eligibility for inclusion. In total, 86 articles were included in the review from 315 papers identified. The review showed that the main uses of photogrammetry in the field of neurosurgery are related to the creation of 3D models of complex neuroanatomical structures and surgical approaches, accompanied by the uses for diagnosis and evaluation of patients with structural deformities of the head and trunk, such as craniosynostosis and scoliosis. Additionally, three instances of photogrammetry applied for more specific aims, namely, cervical spine surgery, skull-base surgery, and radiosurgery, were identified. Information was extracted on the software and systems used to execute the method. With the development of the photogrammetric method, it has become possible to create accurate 3D models of physical objects and analyze images with dedicated software. In the neurosurgical setting, this has translated into the creation of anatomical teaching models and surgical 3D models as well as the evaluation of head and spine deformities. Through those applications, the method has the potential to facilitate the education of residents and medical students and the diagnosis of patient pathologies.

The accuracy of computerized biophotogrammetry in diagnosing changes in the cervical spine and its reliability for the cervical lordosis angle

BACKGROUND

Accuracy studies of biophotogrammetry protocols require standardization similar to radiography.

OBJECTIVE

To estimate the diagnostic accuracy of a biophotogrammetric assessment protocol for cervical hyperlordosis, compared to radiography, and its intra- and inter-examiner reliability for measuring the cervical lordosis angle.

METHODS

A study of diagnostic accuracy in women complaining of cervical pain. Two photos were taken using the CorelDraw biophotogrammetric protocol and one radiograph using the Cobb C1-C7 method. The Intra- and Inter-examiner reliability was calculated using the Kappa index and the intraclass correlation coefficient (ICC). The Bland-Altman plot and the ROC curve were presented.

RESULTS

The sample consisted of 19 women. The accuracy of biophotogrammetry was 94.73% and the reliability between biophotogrammetry and radiography presented an ICC of 0.84 and a Kappa of 0.87. The excellent intra (ICC = 0.94) and inter-examiner (ICC = 0.86) reliability of the biophotogrammetry was confirmed. The area under the ROC curve was 93.5%. The Bland-Altman plot indicated differences between the two instruments close to the mean (1.5∘).

CONCLUSION

The biophotogrammetric protocol proved to be accurate in diagnosing cervical hyperlordosis, with excellent reliability between the biophotogrammetric and radiographic assessments. It also demonstrated excellent intra- and inter-examiner reliability in measuring the cervical lordosis angle.

A mechatronics data collection, image processing, and deep learning platform for clinical posture analysis: a technical note

Static and dynamic posture analysis was a critical clinical examination in physiotherapy and rehabilitation. It was a time-consuming task for clinicians, so a semi-automatic method can facilitate this process as well as provide well-documented medical records and strong infrastructure for deep learning scenarios. The current research presents a mechatronics platform for static and real-time dynamic posture analysis, which consisted of hybrid computational modules. Our study was a developmental and applied research according to a system development life cycle. The designed modules are as follows: (1) a mechanical structure includes patient place, 360-degree engine, mirror, laser, distance meter, and cams; (2) a software module includes data collection, electronic medical record, semi-automatic image analysis, annotation, and reporting, and (3) a network to exchange raw data with deep learning server. Patients were informed about the research by their healthcare provider and all data were transformed into a Fourier format, in which the patients remained autonomous without a bit of information. The results show acceptable reliability and validity of the instruments. Also, a telerehabilitation application was designed to cover the patients after diagnosis. We suggest a longer time for data acquisition. It will lead to a more accurate and fully automated dynamic posture analysis. The result of this study suggest that the designed mechatronics device used in conjunction with smartphone application is a valid tool that can be used to obtain reliable measurements.