Deep learning-based Helicobacter pylori detection: A diagnostic pathology study

Aims: Deep learning (DL), a sub-area of artificial intelligence, has demonstrated great promise at automating diagnostic tasks in pathology, yet its translation into clinical settings has been slow. Few studies have examined its impact on pathologist performance, when embedded into clinical workflows. The identification of H. pylori on H&E stain is a tedious, imprecise task which might benefit from DL assistance. Here, we developed a DL assistant for diagnosing H. pylori in gastric biopsies and tested its impact on pathologist diagnostic accuracy and turnaround time. Methods and results: H&E-stained whole-slide images (WSI) of 303 gastric biopsies with ground truth confirmation by immunohistochemistry formed the study dataset; 47 and 126 WSI were respectively used to train and optimize our DL assistant to detect H. pylori, and 130 were used in a clinical experiment in which 3 experienced GI pathologists reviewed the same test set with and without assistance. On the test set, the assistant achieved high performance, with a WSI-level area-under-the-receiver-operating-characteristic curve (AUROC) of 0.965 (95% CI 0.934-0.987). On H. pylori-positive cases, assisted diagnoses were faster (β, the fixed effect size for assistance= -0.557, p=0.003) and much more accurate (OR=13.37, p<0.001) than unassisted diagnoses. However, assistance increased diagnostic uncertainty on H. pylori-negative cases, resulting in an overall decrease in assisted accuracy (OR=0.435, p=0.016) and negligible impact on overall turnaround time (β for assistance=0.010, p=0.860). Conclusions: DL can assist pathologists with H. pylori diagnosis, but its integration into clinical workflows requires optimization to mitigate diagnostic uncertainty as a potential consequence of assistance.

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Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies

BMC Gastroenterology ◽

10.1186/s12876-020-01494-7 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Sebastian Klein ◽

Jacob Gildenblat ◽

Michaele Angelika Ihle ◽

Sabine Merkelbach-Bruse ◽

Ka-Won Noh ◽

...

Keyword(s):

Helicobacter Pylori ◽

Deep Learning ◽

Decision Support ◽

Ground Truth ◽

Novel Technologies ◽

Therapeutic Decisions ◽

Gastric Biopsies ◽

H Pylori ◽

Microscopic Diagnosis ◽

Whole Slide Images

Abstract Background Helicobacter pylori, a 2 × 1 μm spiral-shaped bacterium, is the most common risk factor for gastric cancer worldwide. Clinically, patients presenting with symptoms of gastritis, routinely undergo gastric biopsies. The following histo-morphological evaluation dictates therapeutic decisions, where antibiotics are used for H. pylori eradication. There is a strong rational to accelerate the detection process of H. pylori on histological specimens, using novel technologies, such as deep learning. Methods We designed a deep-learning-based decision support algorithm that can be applied on regular whole slide images of gastric biopsies. In detail, we can detect H. pylori both on Giemsa- and regular H&E stained whole slide images. Results With the help of our decision support algorithm, we show an increased sensitivity in a subset of 87 cases that underwent additional PCR- and immunohistochemical testing to define a sensitive ground truth of HP presence. For Giemsa stained sections, the decision support algorithm achieved a sensitivity of 100% compared to 68.4% (microscopic diagnosis), with a tolerable specificity of 66.2% for the decision support algorithm compared to 92.6 (microscopic diagnosis). Conclusion Together, we provide the first evidence of a decision support algorithm proving as a sensitive screening option for H. pylori that can potentially aid pathologists to accurately diagnose H. pylori presence on gastric biopsies.

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Deep learning for automated detection and numbering of permanent teeth on panoramic images

Dentomaxillofacial Radiology ◽

10.1259/dmfr.20210296 ◽

2021 ◽

Author(s):

Mohamed Estai ◽

Marc Tennant ◽

Dieter Gebauer ◽

Andrew Brostek ◽

Janardhan Vignarajan ◽

...

Keyword(s):

Deep Learning ◽

High Performance ◽

Detection System ◽

Ground Truth ◽

Automated Detection ◽

Permanent Teeth ◽

Automated Method ◽

Step Procedure ◽

Panoramic Images ◽

Tooth Number

Objective: This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs). Methods: In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs. Results: The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98. Conclusion: The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.

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Deep Learning of COVID-19 Chest X-Rays: New Models or Fine Tuning?

10.36227/techrxiv.12656948.v1 ◽

2020 ◽

Author(s):

Tuan Pham

Keyword(s):

Deep Learning ◽

High Performance ◽

Data Augmentation ◽

Dominant Role ◽

Care Center ◽

Characteristic Curve ◽

Fine Tuning ◽

Urgent Care ◽

X Rays ◽

Training Time

Chest X-rays have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. While many new DL models have been being developed for this purpose, this study aimed to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.

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Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.721460 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yubizhuo Wang ◽

Jiayuan Shao ◽

Pan Wang ◽

Lintao Chen ◽

Mingliang Ying ◽

...

Keyword(s):

Deep Learning ◽

Lymph Node ◽

Hilar Cholangiocarcinoma ◽

High Performance ◽

Regional Lymph Node ◽

Characteristic Curve ◽

Support Vector ◽

Lymph Node Staging ◽

Computed Tomography Images ◽

External Test

BackgroundOur aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients. Methods and MaterialsOf the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastasis stratification classifier (N1 vs. N2) was also proposed with subgroup analysis.ResultsThe average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946.ConclusionsTwo classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.

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A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2019-0004-oa ◽

2019 ◽

Vol 144 (3) ◽

pp. 370-378 ◽

Cited By ~ 7

Author(s):

David R. Martin ◽

Joshua A. Hanson ◽

Rama R. Gullapalli ◽

Fred A. Schultz ◽

Aisha Sethi ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Gold Standard ◽

Phase 1 ◽

Phase 2 ◽

Gastric Biopsies ◽

H Pylori ◽

Sensitivity Specificity ◽

Receiver Operating

Context.— Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched. Objective.— To investigate the use of DL for nonneoplastic gastric biopsies. Design.— Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion. Results.— For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%). Conclusions.— A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

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IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

Sensors ◽

10.3390/s20092556 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2556

Author(s):

Liyang Wang ◽

Yao Mu ◽

Jing Zhao ◽

Xiaoya Wang ◽

Huilian Che

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Real Time ◽

Clinical Symptoms ◽

Characteristic Curve ◽

Learning Model ◽

Machine Learning Algorithms ◽

Test Set ◽

Non Invasive ◽

Deep Learning Model

The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model—referred to as IGRNet—is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.

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Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle

Dentomaxillofacial Radiology ◽

10.1259/dmfr.20200611 ◽

2021 ◽

pp. 20200611

Author(s):

Masako Nishiyama ◽

Kenichiro Ishibashi ◽

Yoshiko Ariji ◽

Motoki Fukuda ◽

Wataru Nishiyama ◽

...

Keyword(s):

Deep Learning ◽

High Performance ◽

Characteristic Curve ◽

Mandibular Condyle ◽

Internal Validity ◽

Classification Performance ◽

Data Sets ◽

Panoramic Radiographs ◽

Significant Difference ◽

Low Performance

Objective: The present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and external validities. Methods: Panoramic radiographs of 100 condyles with and without fractures were collected from two hospitals and a fivefold cross-validation method was employed to construct and evaluate the DL models. The internal and external validities of classification performance were evaluated as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: For internal validity, high classification performance was obtained, with AUC values of >0.85. Conversely, external validity for the data sets from the two hospitals exhibited low performance. Using combined data sets from both hospitals, the DL model exhibited high performance, which was slightly superior or equal to that of the internal validity but without a statistically significant difference. Conclusion: The constructed DL model can be clinically employed for diagnosing fractures of the mandibular condyle using panoramic radiographs. However, the domain shift phenomenon should be considered when generalizing DL systems.

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Deep learning approaches for challenging species and gender identification of mosquito vectors

Scientific Reports ◽

10.1038/s41598-021-84219-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Veerayuth Kittichai ◽

Theerakamol Pengsakul ◽

Kemmapon Chumchuen ◽

Yudthana Samung ◽

Patchara Sriwichai ◽

...

Keyword(s):

Deep Learning ◽

High Performance ◽

Mosquito Species ◽

Characteristic Curve ◽

Confusion Matrix ◽

Morphological Identification ◽

Learning Approaches ◽

Mosquito Vectors ◽

Detection Rates ◽

Proposed Model

AbstractMicroscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

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