Experience with artificial intelligence algorithms for the diagnosis of vertebral compression fractures based on computed tomography: from testing to practical evaluation

Мұқаба

Дәйексөз келтіру

Толық мәтін

Аннотация

BACKGROUND: Osteoporosis is often diagnosed at the stage with complications, i.e., low-energy fractures. Vertebral compression fractures, which are complications of osteoporosis and predictors of subsequent fractures, are often asymptomatic. Compression fractures can be found by computed tomography performed for other indications with vertebral morphometry. Approaches to using artificial intelligence algorithms designed for diagnosing vertebral compression fractures were analyzed.

AIM: Testing artificial intelligence algorithms to conduct morphometric analysis of vertebrae on chest computed tomography scans and assess the possibility of their implementation in medical organizations of the Moscow Healthcare Department.

MATERIALS AND METHODS: To set a clinical task for artificial intelligence algorithms, basic diagnostic requirements in the area of “vertebral compression fractures (osteoporosis)” were formulated. The testing of the artificial intelligence algorithms included the following stages: self-testing, functional and calibration testing, practical evaluation, and operation testing. The first three stages of testing were performed using previously generated datasets. At practical evaluation and operation testing, artificial intelligence algorithms analyzed the data from computed tomography performed in medical organizations. The expert group of radiologists assessed the diagnostic accuracy and functional capacity of the AI algorithms at all stages. The resulting quantitative metrics of the accuracy of artificial intelligence algorithms were compared with the required target values.

RESULTS: From June 2021 to June 2022, two artificial intelligence algorithms (Nos. 1 and 2) with different methods of detecting compression fractures were tested. Both artificial intelligence algorithms successfully passed the self-testing (6 tests), functional (5 tests), and calibration (100 tests) stages. The area under the ROC curve for artificial intelligence algorithm No. 1 was 0.99 (95% CI, 0.98–1), and for artificial intelligence algorithm No. 2, it was 0.91 (95% CI, 0.85–0.96). Artificial intelligence algorithm No. 1 passed the practical evaluation stage without any significant remarks, whereas algorithm No. 2 was sent for fine-tuning. After the operation testing stage, the following accuracy metrics were obtained: the areas under the ROC curve for artificial intelligence algorithm Nos. 1 and 2 were 0.93 (95% CI, 0.89–0.96) and 0.92 (95% CI, 0.90–0.94), respectively. At all stages, both artificial intelligence algorithms demonstrated sufficient metrics for clinical validation.

CONCLUSION: Artificial intelligence algorithms for the automatic diagnosis of vertebral compression fractures have been tested, demonstrating the high quality of their operation. artificial intelligence algorithms can be applied as a supplementary tool in the medical decision support system.

Толық мәтін

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Авторлар туралы

Zlata Artyukova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Хат алмасуға жауапты Автор.
Email: zl.artyukova@gmail.com
ORCID iD: 0000-0003-2960-9787
SPIN-код: 7550-2441
Ресей, Moscow

Alexey Petraikin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: alexeypetraikin@gmail.com
ORCID iD: 0000-0003-1694-4682
SPIN-код: 6193-1656

MD, Dr. Sci. (Medicine), Assistant Professor

Ресей, Moscow

Nikita Kudryavtsev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: KudryavtsevND@zdrav.mos.ru
ORCID iD: 0000-0003-4203-0630
SPIN-код: 1125-8637
Ресей, Moscow

Fedor Petryaykin

Lomonosov Moscow State University

Email: feda.petraykin@gmail.com
ORCID iD: 0000-0001-6923-3839
SPIN-код: 7803-1005
Ресей, Moscow

Dmitry Semenov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: semenovds4@zdrav.mos.ru
ORCID iD: 0000-0002-4293-2514
SPIN-код: 2278-7290

Cand. Sci. (Engineering)

Ресей, Moscow

Daria Sharova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: SharovaDE@zdrav.mos.ru
ORCID iD: 0000-0001-5792-3912
SPIN-код: 1811-7595
Ресей, Moscow

Zhanna Belaya

Endocrinology Research Centre

Email: jannabelaya@gmail.com
ORCID iD: 0000-0002-6674-6441
SPIN-код: 4746-7173

MD, Dr. Sci. (Medicine)

Ресей, Moscow

Anton Vladzimirskyy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies; The First Sechenov Moscow State Medical University

Email: VladzimirskijAV@zdrav.mos.ru
ORCID iD: 0000-0002-2990-7736
SPIN-код: 3602-7120

MD, Dr. Sci. (Medicine)

Ресей, Moscow; Moscow

Yuriy Vasilev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: VasilevYA1@zdrav.mos.ru
ORCID iD: 0000-0002-0208-5218
SPIN-код: 4458-5608

MD, Cand. Sci. (Medicine)

Ресей, Moscow

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1. JATS XML
Жүктеу
2. Fig. 1. Sequence of passing the artificial intelligence service to the stage of trial operation. ERIS EMIAS — Unified radiological information service "Unified medical information and analytical system"; AI-service — artificial intelligence service; CT OGK — computed tomography of the chest organs.

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3. Fig. 2. An example of the Genant-IRA service: an additional curvilinear reconstructed series of a computed tomography study with marking of the target pathology - a compression fracture of the ThXII vertebral body.

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4. Fig. 3. Examples of the HealthVCF service: an additional series of computed tomography studies with marking of the target pathology - compression fracture.

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5. Fig. 4. Results of calculating accuracy metrics with 95% confidence interval: a — Genant-IRA service; b — HealthVCF service.

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6. Fig. 5. ROC curve for determining compression fractures: a — Genant-IRA service (360 studies); b — HealthVCF service (520 studies).

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7. Fig. 6. Examples of errors in the Genant-IRA service: a — false positive result: the service marked the calcified intervertebral disc ThXI–ThXII as the vertebral body ThXII with compression deformation >40% (Genant 3); b — false positive result: the service marked the pronounced osteophyte LI as the vertebral body ThXII with compression deformation >40% (Genant 3), while the vertebral body ThXII is not marked; c — false positive result: a patient with severe scoliosis experienced a critical failure of the algorithm (the so-called algorithm breakdown), a failure in constructing the curvilinear reconstruction, and, as a result, incorrect marking of the vertebrae and assessment of the degree of their compression deformation; d — false positive result: erroneous marking and compression deformation of >40% (Genant 3) of the "bodies" of the ThVII and ThVIII vertebrae was detected due to pronounced ring artefacts due to a malfunction of the computer tomography scanner detector. Data on the scanner defect were forwarded to the technical service of the Moscow City Health Department.

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8. Fig. 7. Examples of errors in the HealthVCF service: a — false positive result and incorrect localization assessment: no compression deformation >25%; b — false negative result (most likely due to severe kyphosis in the patient): the service did not note compression deformation of the vertebral bodies >25% (ThIV, ThV, ThVI, ThVII, LII), notes were made by an expert (red frames); c — false positive result: no compression deformation >25% of the ThIX vertebral body with Schmorl's node; d — false positive result: the service erroneously identified compression deformation >25% of the ThVIII vertebral body due to severe "ring artefacts" caused by a malfunction of the CT scanner detector. Data on the scanner defect were forwarded to the technical service of the Moscow City Health Department.

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