AI Article Synopsis

  • Scoliosis is a 3D spinal deformity typically assessed by measuring the Cobb angle, with advances in image processing improving detection methods.
  • A literature review from four databases identified twelve high-quality studies from 2012 to 2017, focusing on automated, smartphone, and semiautomatic procedures for measuring the Cobb angle.
  • The study concludes that computerized assessment methods, especially semiautomatic ones, are effective for Cobb angle measurement, with no significant difference compared to manual or smartphone methods in accuracy.

Article Abstract

Background: Scoliosis is a three-dimensional deformity of the spine. It is usually assessed by measuring Cobb angle. Nowadays, due to increasing effectiveness of image processing and machine vision, willingness to apply these methods has improved considerably in determining scoliosis of Cobb angle.

Methods: In accordance with the PRISMA guideline, a broad electronic search of all English language literature was conducted on the topic through four databases, including MEDLINE, Web of Science, Scopus, and the Cochrane Library from 2012 (last search date from earlier review) to 30 March 2017.

Results: Twelve studies were included. 90% labeled high-quality were selected for analysis. Eighty percent of the selected studies were published in the period between 2012 and 2017. Three new procedures were used to measure the Cobb angle. One study used automated procedure (7%), two studies used smartphone procedure (14%), and nine studies used the semiautomatic procedure of Cobb measurement (79%). Seven studies reported reproducibility and repeatability. Reproducibility range was 0.72 to 1 in reporting of ICC. Repeatability has a high range in three separated methods.

Conclusion: Computerized assessment method (Automatic and Semi-automatic) is most commonly performed in Cobb measurement. Semi-automatic is an effective measurement option for computerized assessment Cobb angle. There is no significant difference between manual, computer- based, and smartphone-based methods in described measures.

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http://dx.doi.org/10.2174/1573405614666180531073300DOI Listing

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