Scoliosis is a complex 3D spine deformity characterised by an abnormal lateral curvature of the spine and associated rotation of the spine and ribcage. The rotational aspect of scoliosis is most commonly quantified in the Adam's forward flexed position using an analog scoliometer. The scoliometer has a known user error of 5-8°, which is largely dependent on examiner experience, location of curve, patient positioning and BMI. The device is also limited by the 30° scale and parallax errors. Additionally, the scoliometer loses accuracy when the patient's torso cannot be positioned parallel to the ground . This study describes the development of the first digital twin for the analog scoliometer to enable fast, gravity-independent reliable and accurate digital measurements of the Angle of Torso Rotation (ATR) from patient-specific 3D virtual models. A robust semi-automated algorithm of generative design which measures ATR from surface topography was developed. With an operating time of just a few seconds, it provides quick and reliable ATR measurements from simple parametric user inputs. 150 calibrated 3D virtual models of AIS patients treated at the Queensland Children's Hospital Spine Clinic (QCHSC) obtained from our existing database of 3D surface scans (3DSS) and healthy non-scoliotic controls recruited for this study were used to validate the digital scoliometer tool. The tool showed excellent reliability in both intra-user (0.99) and inter-user (0.98) conditions. The digital values had a high positive correlation (0.897) and agreement (92.7%) with the analog ATR measurements made clinically. The tool also showed high sensitivity (95.83%) and specificity (76.76%). The development and validation of this virtual digital tool is significant for telehealth implementation in paediatric spine deformity management and is expected to enhance the remote health management of scoliosis.

The authors have declared no competing interest.

This study received an internal QUT grant from Central for Biomedical Technologies for the analysis of sensitivity and specificity. This study did not receive any external funding.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Hospital and University Human Research Ethics Committee (HREC) approvals were obtained from QCH HREC (LNR/21/QCHQ/75249) and the Queensland University of Technology HREC (Approval number: 4856-HE44) titled Spine Deformity Management Clinical Data Collection Project. Approval to publish deidentified group data analyses by the QCH HREC for the Development of non-invasive monitoring tools for Adolescent Idiopathic Scoliosis, using 3D scanning/photography at the Queensland Children's Hospital was also provided. Informed consent was obtained from all participants and their legal guardians. The methods described in this paper are in accordance with the relevant guidelines and regulations put forth by QCH and QUT HREC and the Declaration of Helsinki.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The raw measurement data and statistical analysis made on the algorithm will be made fully available to the public via QUT's public repository Research Data Finder (RDF) after the paper has been published. Through the RDF, noninstitutional researchers may be granted access to this data after making a request to myself as first author (Dr Sinduja Suresh, s.suresh@qut.edu.au) and owner of the RDF entry. The Rhino-Grasshopper workflow developed in this project is not yet available for public dissemination because we are currently exploring opportunities with local hospitals to include it in a smart device application. However, the workflow is built on commercially available software, and we have described the functionality in detail. We believe that someone with sufficient experience in Grasshopper could replicate this functionality described in the Methods section. The original 3DSS datasets cannot be shared publicly as it may compromise the privacy and confidentiality of our participants.