Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection

Osteoporotic vertebral fractures have a severe impact on patients' overall well-being but are severely under-diagnosed. These fractures present themselves at various levels of severity measured using the Genant's grading scale. Insufficient annotated datasets, severe data-imbalance, and minor difference in appearances between fractured and healthy vertebrae make naive classification approaches result in poor discriminatory performance. Addressing this, we propose a representation learning-inspired approach for automated vertebral fracture detection, aimed at learning latent representations efficient for fracture detection. Building on state-of-art metric losses, we present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme. On a publicly available spine dataset, the proposed loss function achieves a fracture detection F1 score of 81.5%, a 10% increase over a naive classification baseline.Comment: To be presented at MICCAI 202

Применение алгоритма искусственного интеллекта для оценки минеральной плотности тел позвонков по данным компьютерной томографии

Goal: To develop a method for automated assessment of the volumetric bone mineral density (BMD) of the vertebral bodies using an artificial intelligence (AI) algorithm and a phantom modeling method.Materials and Methods: Evaluation of the effectiveness of the AI algorithm designed to assess BMD of the vertebral bodies based on chest CT data. The test data set contains 100 patients aged over 50 y.o.; the ratio between the subjects with/without compression fractures (Сfr) is 48/52. The X-ray density (XRD) of vertebral bodies at T11-L3 was measured by experts and the AI algorithm for 83 patients (205 vertebrae). We used a recently developed QCT PK (Quantitative Computed Tomography Phantom Kalium) method to convert XRD into BMD followed by building calibration lines for seven 64-slice CT scanners. Images were taken from 1853 patients and then processed by the AI algorithm after the calibration. The male to female ratio was 718/1135.Results: The experts and the AI algorithm reached a strong agreement when comparing the measurements of the XRD. The coefficient of determination was R2=0.945 for individual vertebrae (T11-L3) and 0.943 for patients (p=0.000). Once the subjects from the test sample had been separated into groups with/without Сfr, the XRD data yielded similar ROC AUC values for both the experts – 0.880, and the AI algorithm – 0.875. When calibrating CT scanners using a phantom containing BMD samples made of potassium hydrogen phosphate, the following averaged dependence formula BMD =0.77*HU-1.343 was obtained. Taking into account the American College Radiology criteria for osteoporosis, the cut-off value of BMD<80 mg/ml was 105.6HU; for osteopenia BMD<120 mg/ml was 157.6HU. During the opportunistic assessment of BMD in patients aged above 50 years using the AI algorithm, osteoporosis was detected in 31.72% of female and 18.66% of male subjects.Conclusions: This paper demonstrates good comparability for the measurements of the vertebral bodies’ XRD performed by the AI morphometric algorithm and the experts. We presented a method and demonstrated great effectiveness of opportunistic assessment of vertebral bodies’ BMD based on computed tomography data using the AI algorithm and the phantom modeling.Цель работы: разработать методику автоматизированной оценки объемной минеральной плотности кости (МПК) тел позвонков с помощью алгоритма искусственного интеллекта (ИИ) и метода фантомного моделирования.Материалы и методы: Для оценки эффективности алгоритма ИИ, проводящего измерение МПК тел позвонков по данным КТ органов грудной клетки (ОГК), подготовлен набор данных: 100 пациентов старше 50 лет и отношением с/без компрессионных переломов (КП) 48/52. Из них у 83 алгоритмом ИИ и экспертами была измерена рентгеновская плотность (РП) тел позвонков на уровне Th11-L3 (205 позвонков). Для перевода РП (HU) в МПК применялась разработанная ранее методика ККТ ФК (Количественная компьютерная томография фантом калиевый) с построением калибровочных прямых для семи 64-срезовых КТ сканеров. После проведения калибровки были выполнены и обработаны алгоритмом ИИ КТ ОГК 1853 пациентов в соотношении мужчин и женщин составило 718/1135.Результаты: В ходе оценки эффективности алгоритма ИИ получено хорошее соответствие при сравнении измерений МПК по данным экспертов и алгоритма ИИ. Коэффициент детерминации составил R2= 0,945 для отдельных позвонков (Th11-L3) и 0,943 для пациентов (р=0,000). При разделении пациентов из тестовой выборки на группы с/без КП по данным РП были получены сходные показатели ROC AUC для экспертной разметки 0,880 и по данным алгоритма ИИ 0,875. При калибровке КТ сканеров с помощью фантома, содержащего образцы МПК на основе гидрофосфата калия, получена усредненная формула зависимости МПК=0,77*HU-1,343. С учетом критериев American College Radiology для остеопороза граничное значение МПК<80 мг/мл составило 105,6HU для остеопении МПК<120 мг/мл – 157,6HU. При оппортунистическом определении МПК у пациентов старше 50 лет по данным алгоритма ИИ было установлено, что остеопороз выявлен у 31,72% женщин и 18,66% мужчин.Вывод: Продемонстрирована хорошая сопоставимость результатов определения РП тел позвонков по данным морфометрического алгоритма ИИ и при экспертной разметке. Предложена методика и продемонстрирована эффективность оппортунистического определения МПК тел позвонков по данным КТ с помощью алгоритма ИИ и использования фантомного моделирования

CERVICAL SPINE FRACTURE LOCALIZATION USING SEMI-SUPERVISED LEARNING

Cervical spine fracture localization in medical images is a challenging task that requires a large amount of labeled data for accurate diagnosis. However, obtaining labeled data is time-consuming and difficult, which limits the application of supervised learning methods. In this thesis, we propose a semisupervised learning approach to improve the accuracy of cervical spine fracture localization by combining a small amount of labeled data with a larger amount of unlabeled data. Our approach leverages semi-supervised learning techniques to learn patterns and features in the larger set of unlabeled CT scans, which improves the model's ability to generalize to new and unseen cases. Additionally, our approach is more robust to noisy or inaccurate labeled data, as the model can learn to ignore or weight the labeled data based on its confidence in the label. To increase the amount of labeled data available for training, we also explore data augmentation techniques, such as rotation, flipping, cropping. We demonstrate the effectiveness of our approach through experiments on a dataset of CT scans for cervical spine fracture localization. Our results show that our semi-supervised learning approach improves the accuracy of cervical spine fracture localization compared to traditional supervised learning methods, even when trained on a limited amount of labeled data. Overall, our approach has the potential to improve the diagnosis of CSFs in medical images, which can ultimately lead to better patient outcomes

Machine learning in orthopedics: a literature review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance