Quantification of liver fibrosis—a comparative study

Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic

Ensemble convolutional neural network classification for pancreatic steatosis assessment in biopsy images

Non-alcoholic fatty pancreas disease (NAFPD) is a common and at the same time not extensively examined pathological condition that is significantly associated with obesity, metabolic syndrome, and insulin resistance. These factors can lead to the development of critical pathogens such as type-2 diabetes mellitus (T2DM), atherosclerosis, acute pancreatitis, and pancreatic cancer. Until recently, the diagnosis of NAFPD was based on noninvasive medical imaging methods and visual evaluations of microscopic histological samples. The present study focuses on the quantification of steatosis prevalence in pancreatic biopsy specimens with varying degrees of NAFPD. All quantification results are extracted using a methodology consisting of digital image processing and transfer learning in pretrained convolutional neural networks for the detection of histological fat structures. The proposed method is applied to 20 digitized histological samples, producing an 0.08% mean fat quantification error thanks to an ensemble CNN voting system and 83.3% mean Dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease

Wearable devices can capture objective day-to-day data about Parkinson’s Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age ≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort

Freezing of gait and fall detection in Parkinson’s disease using wearable sensors:a systematic review

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson’s disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73–100% for sensitivity and 67–100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets

High-throughput, machine learning-based quantification of steatosis, inflammation, ballooning, and fibrosis in biopsies from patients with nonalcoholic fatty liver disease

Background & Aims Liver biopsy is the reference standard for staging and grading non-alcoholic fatty liver disease (NAFLD), but histologic scoring systems are semi-quantitative with marked inter- and intra-observer variation. We used machine learning to develop fully automated software for quantification of steatosis, inflammation, ballooning, and fibrosis in biopsies from patients with NAFLD and validated the technology in a separate group of patients. Methods We collected data from 246 consecutive patients with biopsy-proven NAFLD and followed in London, the United Kingdom, from January 2010 through December 2016. Biopsies from the first 100 patients were used to derive the algorithm and biopsies from the following 146 were used to validate it. Biopsies were independently scored by pathologists using the nonalcoholic steatohepatitis clinical research network criteria and digitalized. Areas of steatosis, inflammation, ballooning, and fibrosis were annotated on biopsies by 2 hepatobiliary histopathologists to facilitate machine learning. Images of biopsies from the derivation and validation sets were then analyzed by the algorithm to compute percentages of fat, inflammation, ballooning, and fibrosis, as well as collagen proportionate area (CPA), and compared with findings from pathologists’ manual annotations and conventional scoring systems. Results In the derivation group, results from manual annotation and the software had an inter-class correlation coefficient (ICC) of 0.97 for steatosis (95%CI, 0.95–0.99; P<.001); ICC, 0.96 for inflammation (95%CI, 0.9–0.98; P<.001); ICC, 0.94 for ballooning (95%CI, 0.87–0.98; P<.001); and ICC, 0.92 for fibrosis (95%CI, 0.88–0.96; P=.001). Percentages of fat, inflammation, ballooning, and CPA from the derivation group were confirmed in the validation cohort. The software identified histological features of NAFLD with levels of inter- and intra-observer agreement ranging from 0.95 to 0.99; this value was higher than that of semi-quantitative scoring systems which ranged 0.58 to 0.88. In a subgroup of paired liver biopsies, quantitative analysis was more sensitive in detecting differences compared to the nonalcoholic steatohepatitis Clinical Research Network scoring system. Conclusions We used machine learning to develop software to rapidly and objectively analyse liver biopsies for histologic features of NAFLD. The results from the software correlate with those from histopathologists, with high levels of inter- and intra-observer agreement. Findings were validated in a separate group of patients. This tool might be used for objective assessment of response to therapy for NAFLD in practice and clinical trials