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Pathology Pathology

Prediction of the Age at Onset of Spinocerebellar Ataxia Type 3 with Machine Learning.

In Movement disorders : official journal of the Movement Disorder Society

BACKGROUND : In polyglutamine (polyQ) disease, the investigation of the prediction of a patient's age at onset (AAO) facilitates the development of disease-modifying intervention and underpins the delay of disease onset and progression. Few polyQ disease studies have evaluated AAO predicted by machine-learning algorithms and linear regression methods.

OBJECTIVE : The objective of this study was to develop a machine-learning model for AAO prediction in the largest spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) population from mainland China.

METHODS : In this observational study, we introduced an innovative approach by systematically comparing the performance of 7 machine-learning algorithms with linear regression to explore AAO prediction in SCA3/MJD using CAG expansions of 10 polyQ-related genes, sex, and parental origin.

RESULTS : Similar prediction performance of testing set and training set in each models were identified and few overfitting of training data was observed. Overall, the machine-learning-based XGBoost model exhibited the most favorable performance in AAO prediction over the traditional linear regression method and other 6 machine-learning algorithms for the training set and testing set. The optimal XGBoost model achieved mean absolute error, root mean square error, and median absolute error of 5.56, 7.13, 4.15 years, respectively, in testing set 1, with mean absolute error (4.78 years), root mean square error (6.31 years), and median absolute error (3.59 years) in testing set 2.

CONCLUSION : Machine-learning algorithms can be used to predict AAO in patients with SCA3/MJD. The optimal XGBoost algorithm can provide a good reference for the establishment and optimization of prediction models for SCA3/MJD or other polyQ diseases. © 2020 International Parkinson and Movement Disorder Society.

Peng Linliu, Chen Zhao, Chen Tiankai, Lei Lijing, Long Zhe, Liu Mingjie, Deng Qi, Yuan Hongyu, Zou Guangdong, Wan Linlin, Wang Chunrong, Peng Huirong, Shi Yuting, Wang Puzhi, Peng Yun, Wang Shang, He Lang, Xie Yue, Tang Zhichao, Wan Na, Gong Yiqing, Hou Xuan, Shen Lu, Xia Kun, Li Jinchen, Chen Chao, Zhang Zuping, Qiu Rong, Tang Beisha, Jiang Hong


spinocerebellar ataxia type 3/Machado-Joseph disease; CAG repeats; age at onset prediction; machine learning

General General

Bruise dating using deep learning.

In Journal of forensic sciences

The bruise dating can have important medicolegal implications in family violence and violence against women cases. However, studies show that the medical specialist has 50% accuracy in classifying a bruise by age, mainly due to the variability of the images and the color of the bruise. This research proposes a model, based on deep convolutional neural networks, for bruise dating using only images, by age ranges, ranging from 0-2 days to 17-30 days, and images of healthy skin. A 2140 experimental bruise photograph dataset was constructed, for which a data capture protocol and a preprocessing procedure are proposed. Similarly, 20 classification models were trained with the Inception V3, Resnet50, MobileNet, and MnasNet architectures, where combinations of learning transfer, cross-validation, and data augmentation were used. Numerical experiments show that classification models based on MnasNet have better results, reaching 97.00% precision and sensitivity, and 99.50% specificity, exceeding 40% precision reported in the literature. Also, it was observed that the precision of the model decreases with the age of the bruise.

Tirado Jhonatan, Mauricio David


MasNet, bruise dating, convolutional neural network, deep learning

Radiology Radiology

Potential use of deep learning techniques for postmortem imaging.

In Forensic science, medicine, and pathology

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work.

Dobay Akos, Ford Jonathan, Decker Summer, Ampanozi Garyfalia, Franckenberg Sabine, Affolter Raffael, Sieberth Till, Ebert Lars C


Computed tomography, Convolutional neural networks, Deep learning, Forensic sciences, PMCT

General General

A novel semi-supervised multi-view clustering framework for screening Parkinson's disease.

In Mathematical biosciences and engineering : MBE

In recent years, there are many research cases for the diagnosis of Parkinson's disease (PD) with the brain magnetic resonance imaging (MRI) by utilizing the traditional unsupervised machine learning methods and the supervised deep learning models. However, unsupervised learning methods are not good at extracting accurate features among MRIs and it is difficult to collect enough data in the field of PD to satisfy the need of training deep learning models. Moreover, most of the existing studies are based on single-view MRI data, of which data characteristics are not sufficient enough. In this paper, therefore, in order to tackle the drawbacks mentioned above, we propose a novel semi-supervised learning framework called Semi-supervised Multi-view learning Clustering architecture technology (SMC). The model firstly introduces the sliding window method to grasp different features, and then uses the dimensionality reduction algorithms of Linear Discriminant Analysis (LDA) to process the data with different features. Finally, the traditional single-view clustering and multi-view clustering methods are employed on multiple feature views to obtain the results. Experiments show that our proposed method is superior to the state-of-art unsupervised learning models on the clustering effect. As a result, it may be noted that, our work could contribute to improving the effectiveness of identifying PD by previous labeled and subsequent unlabeled medical MRI data in the realistic medical environment.

Zhang Xiao Bo, Zhai Dong Hai, Yang Yan, Zhang Yi Ling, Wang Chun Lin


** Parkinson’s disease (PD) , clustering , dimensionality reduction , feature extraction , semi-supervised learning **

General General

Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning-based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading.

METHODS : A total of 121 clinical routines [18F]Florbetaben PET images were collected for the randomized blind-reader study. The amyloid PET images were visually interpreted by three experts independently blind to other information. The readers qualitatively interpreted images without quantification at the first reading session. After more than 2-week interval, the readers additionally interpreted images with the quantification results provided by the deep learning system. The qualitative assessment was based on a 3-point BAPL score (1: no amyloid load, 2: minor amyloid load, and 3: significant amyloid load). The confidence score for each session was evaluated by a 3-point score (0: ambiguous, 1: probably, and 2: definite to decide).

RESULTS : Inter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27 ± 0.078 for visual reading-only session vs. 1.66 ± 0.63 for a visual reading session with the deep learning system).

CONCLUSION : Our results highlight the impact of deep learning-based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading.

Kim Ji-Young, Oh Dongkyu, Sung Kiyoung, Choi Hongyoon, Paeng Jin Chul, Cheon Gi Jeong, Kang Keon Wook, Lee Dong Young, Lee Dong Soo


Alzheimer’s disease, Amyloid PET, Deep learning, PET, Visual quantification, [18F]Florbetaben

General General

Beyond abstinence and relapse: cluster analysis of drug-use patterns during treatment as an outcome measure for clinical trials.

In Psychopharmacology

RATIONALE : Many people being treated for opioid use disorder continue to use drugs during treatment. This use occurs in patterns that rarely conform to well-defined cycles of abstinence and relapse. Systematic identification and evaluation of these patterns could enhance analysis of clinical trials and provide insight into drug use.

OBJECTIVES : To evaluate such an approach, we analyzed patterns of opioid and cocaine use from three randomized clinical trials of contingency management in methadone-treated participants.

METHODS : Sequences of drug test results were analyzed with unsupervised machine-learning techniques, including hierarchical clustering of categorical results (i.e., whether any samples were positive during each week) and K-means longitudinal clustering of quantitative results (i.e., the proportion positive each week). The sensitivity of cluster membership as an experimental outcome was assessed based on the effects of contingency management. External validation of clusters was based on drug craving and other symptoms of substance use disorder.

RESULTS : In each clinical trial, we identified four clusters of use patterns, which can be described as opioid use, cocaine use, dual use (opioid and cocaine), and partial/complete abstinence. Different clustering techniques produced substantially similar classifications of individual participants, with strong above-chance agreement. Contingency management increased membership in clusters with lower levels of drug use and fewer symptoms of substance use disorder.

CONCLUSIONS : Cluster analysis provides person-level output that is more interpretable and actionable than traditional outcome measures, providing a concrete answer to the question of what clinicians can tell patients about the success rates of new treatments.

Panlilio Leigh V, Stull Samuel W, Bertz Jeremiah W, Burgess-Hull Albert J, Kowalczyk William J, Phillips Karran A, Epstein David H, Preston Kenzie L


Cluster analysis, Cocaine, Contingency management, Methadone, Opioids, Substance use disorder, Treatment outcomes