Water monitoring: automated and real time identification and classification of algae using digital microscopy

2014 ◽  
Vol 16 (11) ◽  
pp. 2656-2665 ◽  
Author(s):  
Primo Coltelli ◽  
Laura Barsanti ◽  
Valtere Evangelista ◽  
Anna Maria Frassanito ◽  
Paolo Gualtieri
Keyword(s):  
Environmental Monitoring ◽  
Real Time ◽  
Water Monitoring ◽  
Digital Microscopy ◽  
Innovative System ◽  
Real Time Identification

This paper presents an innovative system, providing a reliable, real time recognition of multi-algal samples for environmental monitoring purposes.

Water monitoring by means of digital microscopy identification and classification of microalgae

2021 ◽  
Author(s):  
Laura Barsanti ◽  
Lorenzo Birindelli ◽  
Paolo Gualtieri
Keyword(s):  
Water Monitoring ◽  
Digital Microscopy ◽  
Freshwater Microalgae ◽  
Diverse Groups

Marine and freshwater microalgae belong to taxonomically and morphologically diverse groups of organisms spanning many phyla with thousands of species. These organisms play an important role as indicators of water...

scholarly journals Real-Time Identification and Classification of Road Surface with Neural Network.

1993 ◽  
Vol 59 (564) ◽  
pp. 2353-2360
Author(s):  
Toshinari Shiotsuka ◽  
Takahiro Kobayashi ◽  
Kazuo Yoshida ◽  
Akio Nagamatsu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Road Surface ◽  
Real Time Identification

Real Time Identification and Classification of Road Surface with Neural Network

1993 ◽  
Author(s):  
Toshinari Shiotsuka ◽  
Akio Nagamatsu ◽  
Kazuo Yoshida
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Road Surface ◽  
Real Time Identification

scholarly journals Real-Time Identification of Patients Included in the CMS Bundled Payment Care Improvement (BPCI) Program

2020 ◽  
Vol 41 (S1) ◽  
pp. s367-s368
Author(s):  
Michael Korvink ◽  
John Martin ◽  
Michael Long
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Care Providers ◽  
Meta Model ◽  
Bundled Payment ◽  
Clinical Cohort ◽  
Real Time Identification ◽  
Care Improvement

Background: The Bundled Payment Care Improvement Program is a CMS initiative designed to encourage greater collaboration across settings of care, especially as it relates to an initial set of targeted clinical episodes, which include sepsis and pneumonia. As with many CMS incentive programs, performance evaluation is retrospective in nature, resulting in after-the-fact changes in operational processes to improve both efficiency and quality. Although retrospective performance evaluation is informative, care providers would ideally identify a patient’s potential clinical cohort during the index stay and implement care management procedures as necessary to prevent or reduce the severity of the condition. The primary challenges for real-time identification of a patient’s clinical cohort are CMS-targeted cohorts are based on either MS-DRG (grouping of ICD-10 codes) or HCPCS coding—coding that occurs after discharge by clinical abstractors. Additionally, many informative data elements in the EHR lack standardization and no simple and reliable heuristic rules can be employed to meaningfully identify those cohorts without human review. Objective: To share the results of an ensemble statistical model to predict patient risks of sepsis and pneumonia during their hospital (ie, index) stay. Methods: The predictive model uses a combination of Bernoulli Naïve Bayes natural language processing (NLP) classifiers, to reduce text dimensionality into a single probability value, and an eXtreme Gradient Boosting (XGBoost) algorithm as a meta-model to collectively evaluate both standardized clinical elements alongside the NLP-based text probabilities. Results: Bernoulli Naïve Bayes classifiers have proven to perform well on short text strings and allow for highly explanatory unstructured or semistructured text fields (eg, reason for visit, culture results), to be used in a both comparative and generalizable way within the larger XGBoost model. Conclusions: The choice of XGBoost as the meta-model has the benefits of mitigating concerns of nonlinearity among clinical features, reducing potential of overfitting, while allowing missing values to exist within the data. Both the Bayesian classifier and meta-model were trained using a patient-level integrated dataset extracted from both a patient-billing and EHR data warehouse maintained by Premier. The data set, joined by patient admission-date, medical record number, date of birth, and hospital entity code, allows the presence of both the coded clinical cohort (derived from the MS-DRG) and the explanatory features in the EHR to exist within a single patient encounter record. The resulting model produced F1 performance scores of .65 for the sepsis population and .61 for the pneumonia population.Funding: NoneDisclosures: None

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

scholarly journals Classification of endonasal HHT lesions using digital microscopy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
F. Haubner ◽  
A. Schneider ◽  
H. Schinke ◽  
M. Bertlich ◽  
B. G. Weiss ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Treatment Modalities ◽  
Hereditary Haemorrhagic Telangiectasia ◽  
Digital Microscopy ◽  
Digital Microscope ◽  
Lower Severity ◽  
Gender Specific Differences ◽  
Vegf Level

Abstract Background Recurrent spontaneous epistaxis is the most common clinical manifestation and the most debilitating symptom in hereditary haemorrhagic telangiectasia (HHT) patients. To this date, there exist only a classification of HHT patients by different genetic mutations. There is no standard classification for the mucocutaneous endonasal manifestations of HHT. The aim of the present study was to document the variety of endonasal HHT lesions using digital microscopy and to propose a clinical classification. Methods We recorded the endonasal HHT lesions of 28 patients using a digital microscope. We reconstructed the 3D images und videos recorded by digital microscope afterwards and classified the endonasal lesions of HHT in two classes: Grade A, presence of only flat telangiectasias in the mucosa level and Grade B, (additional) presence of raised berry or wart-like telangiectasia spots. We investigated also Haemoglobin level by routine laboratory procedures, plasma VEGF level by ELISA, Severity of epistaxis by epistaxis severity score (ESS) and quality of life by a linear visual analogue scale (VAS). Results We found a higher quality of life and a lower severity of epistaxis in Grade A patients in comparison to Grade B patients. No difference in plasma VEGF level and in Haemoglobin between Grad A patients and Grade B patients could be detected. Plasma VEGF levels showed no gender specific differences. It could also not be correlated to the extranasal manifestation. Conclusion The classification for endonasal manifestation of HHT proposed in this study indicates severity of epistaxis und quality of life. Digital microscopy with the ability of 3D reconstruction of images presents a useful tool for such classifications. The classification of endonasal HHT lesions using digital microscopy allows to evaluate the dynamic of HHT lesions in the course of time independent of examiner. This allows also to evaluate the efficacy of the different treatment modalities by dynamic of HHT lesions. Moreover digital microscopy is very beneficial in academic teaching of rare diseases.

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