Automated histopathological evaluation of pterygium using artificial intelligence

PurposeThis study aimed to evaluate the efficacy of a new automated method for the evaluation of histopathological images of pterygium using artificial intelligence.MethodsAn in-house software for automated grading of histopathological images was developed. Histopathological images of pterygium (400 images from 40 patients) were analysed using our newly developed software. Manual grading (I–IV), labelled based on an established scoring system, served as the ground truth for training the four-grade classification models. Region of interest segmentation was performed before the classification of grades, which was achieved by the combination of expectation-maximisation and k-nearest neighbours. Fifty-five radiomic features extracted from each image were analysed with feature selection methods to examine the significant features. Five classifiers were evaluated for their ability to predict quantitative grading.ResultsAmong the classifier models applied for automated grading in this study, the bagging tree showed the best performance, with a 75.9% true positive rate (TPR) and 75.8% positive predictive value (PPV) in internal validation. In external validation, the method also demonstrated reproducibility, with an 81.3% TPR and 82.0% PPV for the average of four classification grades.ConclusionsOur newly developed automated method for quantitative grading of histopathological images of pterygium may be a reliable method for quantitative analysis of histopathological evaluation of pterygium.

Learning from EMG: semi-automated grading of facial nerve function

The current grading of facial nerve function is based on subjective impression with the established assessment scale of House and Brackmann (HB). Especially for research a more objective method is needed to lower the interobserver variability to a minimum. We developed a semi-automated grading system based on (facial) surface EMG-data measuring the facial nerve function of 28 patients with vestibular schwannoma surgery. The sEMG was recorded preoperatively, postoperatively and after 3–12 months. In addition, the HB grade was determined. After manual selection and preprocessing, the data were subjected to machine learning classificators (Logistic regression, SVM and KNN). Lateralization indices were calculated and multivariant machine learning analysis was performed according to three scenarios [differentiation of normal (1) and slight (2) vs. impaired facial nerve function and classification of HB 1-3 (3)]. The calculated AUC for each scenario showed overall good differentiation capability with a median AUC of 0.72 for scenario 1, 0.91 for scenario 2 and multiclass AUC of 0.74 for scenario 3. This study approach using sEMG and machine learning shows feasibility regarding facial nerve grading in perioperative VS-surgery setting. sEMG may be a viable alternative to House Brackmann regarding objective evaluation of facial function especially for research purposes.

Measuring Shear Wave Splitting Using the Silver and Chan Method

<p>Seismic shear waves emitted by earthquakes can be modelled as plane (transverse) waves. When entering an anisotropic medium they can be split into two orthogonal components moving at different speeds. This splitting occurs along an axis, the fast direction, that is determined by the ambient tectonic stress. Shear wave splitting is thus a commonly used tool for examining tectonic stress in the Earth’s interior. A common technique used to measure shear wave splitting is the Silver and Chan (1991) method. However, there is little literature assessing the robustness of this method, particularly for its use with local earthquakes, and the quality of results can vary. We present here a comprehensive analysis of the Silver and Chan method comprising theoretical derivations and statistical tests of the assumptions behind this method. We then produce an automated grading system calibrated against an expert manual grader using multiple linear regression. We find that there are errors in the derivation of certain equations in the Silver and Chan method and that it produces biased estimates of the errors. Further, the assumptions used to generate the errors do not hold. However, for high quality results (earthquake events where the signal is strong and the earthquake geometry is optimal), the standard errors are representative of the spread in the parameter estimates. Also, we find that our automated grading method produces grades that match the manual grades, and is able to identify mistakes in the manual grades by detecting substantial inconsistencies with the automated grades.</p>

Measuring Shear Wave Splitting Using the Silver and Chan Method

<p>Seismic shear waves emitted by earthquakes can be modelled as plane (transverse) waves. When entering an anisotropic medium they can be split into two orthogonal components moving at different speeds. This splitting occurs along an axis, the fast direction, that is determined by the ambient tectonic stress. Shear wave splitting is thus a commonly used tool for examining tectonic stress in the Earth’s interior. A common technique used to measure shear wave splitting is the Silver and Chan (1991) method. However, there is little literature assessing the robustness of this method, particularly for its use with local earthquakes, and the quality of results can vary. We present here a comprehensive analysis of the Silver and Chan method comprising theoretical derivations and statistical tests of the assumptions behind this method. We then produce an automated grading system calibrated against an expert manual grader using multiple linear regression. We find that there are errors in the derivation of certain equations in the Silver and Chan method and that it produces biased estimates of the errors. Further, the assumptions used to generate the errors do not hold. However, for high quality results (earthquake events where the signal is strong and the earthquake geometry is optimal), the standard errors are representative of the spread in the parameter estimates. Also, we find that our automated grading method produces grades that match the manual grades, and is able to identify mistakes in the manual grades by detecting substantial inconsistencies with the automated grades.</p>

Pancreatic cancer grading in pathological images using deep learning convolutional neural networks

Background: Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. Methods: A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. Results: From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. Conclusions: To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.