Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology.

BACKGROUND AND AIMS: Accurate classification of plaque composition is essential for treatment planning. Intravascular ultrasound (IVUS) has limited efficacy in assessing tissue types, while near-infrared spectroscopy (NIRS) provides complementary information to IVUS but lacks depth information. The aim of this study is to train and assess the efficacy of a machine learning classifier for plaque component classification that relies on IVUS echogenicity and NIRS-signal, using histology as reference standard. METHODS: Matched NIRS-IVUS and histology images from 15 cadaveric human coronary arteries were analyzed (10 vessels were used for training and 5 for testing). Fibrous/pathological intimal thickening (F-PIT), early necrotic core (ENC), late necrotic core (LNC), and calcific tissue regions-of-interest were detected on histology and superimposed onto IVUS frames. The pixel intensities of these tissue types from the training set were used to train a J48 classifier for plaque characterization (ECHO-classification). To aid differentiation of F-PIT from necrotic cores, the NIRS-signal was used to classify non-calcific pixels outside yellow-spot regions as F-PIT (ECHO-NIRS classification). The performance of ECHO and ECHO-NIRS classifications were validated against histology. RESULTS: 262 matched frames were included in the analysis (162 constituted the training set and 100 the test set). The pixel intensities of F-PIT and ENC were similar and thus these two tissues could not be differentiated by echogenicity. With ENC and LNC as a single class, ECHO-classification showed good agreement with histology for detecting calcific and F-PIT tissues but had poor efficacy for necrotic cores (recall 0.59 and precision 0.29). Similar results were found when F-PIT and ENC were treated as a single class (recall and precision for LNC 0.78 and 0.33, respectively). ECHO-NIRS classification improved necrotic core and LNC detection, resulting in an increase of the overall accuracy of both models, from 81.4% to 91.8%, and from 87.9% to 94.7%, respectively. Comparable performance of the two models was seen in the test set where the overall accuracy of ECHO-NIRS classification was 95.0% and 95.5%, respectively. CONCLUSIONS: The combination of echogenicity with NIRS-signal appears capable of overcoming limitations of echogenicity, enabling more accurate characterization of plaque components.

Immobilisation in occipital condyle fractures: A systematic review.

OBJECTIVES: The objectives of this review are to determine the level of evidence for the management of OCF, compare outcomes of different immobilisation, and to review the prognosis. PATIENTS AND METHODS: A literature search was conducted using 3 databases (MEDLINE, PubMed and EMBASE). All papers between 1940 and July 2017 were screened using PRISMA guidelines. Inclusion criteria were patients with a confirmed diagnosis of occipital condyle fracture(s) on CT managed with any form of immobilisation with no age restriction. Primary outcome was clinical improvement in symptoms or Neck Disability Index. MINORS and OCEBM level was assigned to each study. RESULTS: 25 studies met the inclusion criteria. Most studies used a single form of C-spine immobilisation support (58%) with a semi rigid collar and halo device being the most common. From these studies, the average length of time for immobilisation was 11.7 weeks, 9 weeks and 8.3 weeks for halo, semi-rigid and rigid cervical collars respectively. Neuro deficit was found in 20.3% of patients. OCEBM level of evidence and MINORS score was low. CONCLUSION: Management of OCF is associated with low level of evidence. Further studies are needed to determine optimal management of these under-diagnosed fractures.