scholarly journals Comparison of a Deep Learning Algorithm vs. Humans for Vertebral Heart Scale Measurements in Cats and Dogs Shows a High Degree of Agreement Among Readers

2021 ◽  
Vol 8 ◽  
Author(s):  
Emilie Boissady ◽  
Alois De La Comble ◽  
Xiajuan Zhu ◽  
Jonathan Abbott ◽  
Hespel Adrien-Maxence
Keyword(s):  
Learning Algorithm ◽  
Intraclass Correlation ◽  
Ground Truth ◽  
Reference Points ◽  
Deep Learning Algorithm ◽  
High Degree ◽  
Manual Calculation ◽  
Original Approach ◽  
Cardiac Enlargement

Heart disease is a leading cause of death among cats and dogs. Vertebral heart scale (VHS) is one tool to quantify radiographic cardiac enlargement and to predict the occurrence of congestive heart failure. The aim of this study was to evaluate the performance of artificial intelligence (AI) performing VHS measurements when compared with two board-certified specialists. Ground truth consisted of the average of constituent VHS measurements performed by board-certified specialists. Thirty canine and 30 feline thoracic lateral radiographs were evaluated by each operator, using two different methods for determination of the cardiac short axis on dogs' radiographs: the original approach published by Buchanan and the modified approach proposed by the EPIC trial authors, and only Buchanan's method for cats' radiographs. Overall, the VHS calculated by the AI, radiologist, and cardiologist had a high degree of agreement in both canine and feline patients (intraclass correlation coefficient (ICC) = 0.998). In canine patients, when comparing methods used to calculate VHS by specialists, there was also a high degree of agreement (ICC = 0.999). When evaluating specifically the results of the AI VHS vs. the two specialists' readings, the agreement was excellent for both canine (ICC = 0.998) and feline radiographs (ICC = 0.998). Performance of AI trained to locate VHS reference points agreed with manual calculation by specialists in both cats and dogs. Such a computer-aided technique might be an important asset for veterinarians in general practice to limit interobserver variability and obtain more comparable VHS reading over time.

scholarly journals Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246472
Author(s):  
Eun Young Kim ◽  
Young Jae Kim ◽  
Won-Jun Choi ◽  
Gi Pyo Lee ◽  
Ye Ra Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Health Screening ◽  
Health Care Clinics ◽  
Radiologic Findings ◽  
Deep Learning Algorithm ◽  
Chest X Ray ◽  
Good Stand

Purpose This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities on chest X-ray (CXR) using a consecutively collected multicenter health screening cohort. Methods and materials A consecutive health screening cohort of participants who underwent both CXR and chest computed tomography (CT) within 1 month was retrospectively collected from three institutions’ health care clinics (n = 5,887). Referable thoracic abnormalities were defined as any radiologic findings requiring further diagnostic evaluation or management, including DLA-target lesions of nodule/mass, consolidation, or pneumothorax. We evaluated the diagnostic performance of the DLA for referable thoracic abnormalities using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity using ground truth based on chest CT (CT-GT). In addition, for CT-GT-positive cases, three independent radiologist readings were performed on CXR and clear visible (when more than two radiologists called) and visible (at least one radiologist called) abnormalities were defined as CXR-GTs (clear visible CXR-GT and visible CXR-GT, respectively) to evaluate the performance of the DLA. Results Among 5,887 subjects (4,329 males; mean age 54±11 years), referable thoracic abnormalities were found in 618 (10.5%) based on CT-GT. DLA-target lesions were observed in 223 (4.0%), nodule/mass in 202 (3.4%), consolidation in 31 (0.5%), pneumothorax in one 1 (<0.1%), and DLA-non-target lesions in 409 (6.9%). For referable thoracic abnormalities based on CT-GT, the DLA showed an AUC of 0.771 (95% confidence interval [CI], 0.751–0.791), a sensitivity of 69.6%, and a specificity of 74.0%. Based on CXR-GT, the prevalence of referable thoracic abnormalities decreased, with visible and clear visible abnormalities found in 405 (6.9%) and 227 (3.9%) cases, respectively. The performance of the DLA increased significantly when using CXR-GTs, with an AUC of 0.839 (95% CI, 0.829–0.848), a sensitivity of 82.7%, and s specificity of 73.2% based on visible CXR-GT and an AUC of 0.872 (95% CI, 0.863–0.880, P <0.001 for the AUC comparison of GT-CT vs. clear visible CXR-GT), a sensitivity of 83.3%, and a specificity of 78.8% based on clear visible CXR-GT. Conclusion The DLA provided fair-to-good stand-alone performance for the detection of referable thoracic abnormalities in a multicenter consecutive health screening cohort. The DLA showed varied performance according to the different methods of ground truth.

Segmented and Non-Segmented Skin Lesions Classification Using Transfer Learning and Adaptive Moment Learning Rate Technique Using Pretrained Convolutional Neural Network

2019 ◽  
Vol 42 ◽  
pp. 67-78 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Hiam Alquraan ◽  
Isam Abu Qasmieh
Keyword(s):  
Skin Lesion ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Skin Lesions ◽  
Learning Rate ◽  
Seborrheic Keratosis ◽  
Severe Problem ◽  
Deep Learning Algorithm ◽  
Reliable Classification ◽  
High Degree

A skin lesion is a very severe problem, especially in coastal countries. Early detection by a highly reliable classification of skin lesion causes a great reduction in the mortality rate. Recognition of melanoma is a complicated issue due to the high degree of visual similarities between melanoma and non-melanoma lesions. Various studies are carried out to overcome this problem and to obtain accurate screening of skin lesion, where the most recent method for segmenting and classifying the lesion is based on a deep learning algorithm. In this paper, (GoogleNet) and (AlexNet) are employed with transfer learning and optimization gradient descent adaptive momentum learning rate (ADAM). The proposed method is applied on Archive International Skin Imaging Collaboration (ISIC) database to classify images into three main classes (benign, melanoma, seborrheic keratosis) under the two scenarios; segmented and non-segmented lesion images. The overall accuracy of the non-segmented classification database is 92.2% and 89.8% for the non-segmented dataset. Utilizing optimization algorithm (ADAM) leads to a significant improvement in the classification results when they are compared with previous studies.

scholarly journals Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2194
Author(s):  
Asim Khan ◽  
Warda Asim ◽  
Anwaar Ulhaq ◽  
Bilal Ghazi ◽  
Randall W. Robinson
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Ground Truth ◽  
Urban Ecosystem ◽  
City Council ◽  
Local Council ◽  
Deep Learning Algorithm ◽  
Urban Greenery

Urban greenery is an essential characteristic of the urban ecosystem, which offers various advantages, such as improved air quality, human health facilities, storm-water run-off control, carbon reduction, and an increase in property values. Therefore, identification and continuous monitoring of the vegetation (trees) is of vital importance for our urban lifestyle. This paper proposes a deep learning-based network, Siamese convolutional neural network (SCNN), combined with a modified brute-force-based line-of-bearing (LOB) algorithm that evaluates the health of Eucalyptus trees as healthy or unhealthy and identifies their geolocation in real time from Google Street View (GSV) and ground truth images. Our dataset represents Eucalyptus trees’ various details from multiple viewpoints, scales and different shapes to texture. The experiments were carried out in the Wyndham city council area in the state of Victoria, Australia. Our approach obtained an average accuracy of 93.2% in identifying healthy and unhealthy trees after training on around 4500 images and testing on 500 images. This study helps in identifying the Eucalyptus tree with health issues or dead trees in an automated way that can facilitate urban green management and assist the local council to make decisions about plantation and improvements in looking after trees. Overall, this study shows that even in a complex background, most healthy and unhealthy Eucalyptus trees can be detected by our deep learning algorithm in real time.

scholarly journals Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prashant Sadashiv Gidde ◽  
Shyam Sunder Prasad ◽  
Ajay Pratap Singh ◽  
Nitin Bhatheja ◽  
Satyartha Prakash ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Training Data ◽  
X Rays ◽  
Rt Pcr ◽  
Deep Learning Algorithm ◽  
High Throughput Detection ◽  
Automated Screening ◽  
Pneumonia Diagnosis

AbstractSARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

scholarly journals Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement

2019 ◽  
Vol 21 (11) ◽  
pp. 1412-1422 ◽  
Author(s):  
Ken Chang ◽  
Andrew L Beers ◽  
Harrison X Bai ◽  
James M Brown ◽  
K Ina Ly ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tumor Volume ◽  
Learning Algorithm ◽  
Intraclass Correlation ◽  
Response Assessment ◽  
Tumor Burden ◽  
Automatic Assessment ◽  
Postoperative Mri ◽  
Longitudinal Measurement ◽  
Deep Learning Algorithm

Abstract Background Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). Methods Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment “baseline” MRIs) from 1 institution. Results The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. Conclusions Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.

scholarly journals Robustness of Deep Learning Algorithm to Varying Imaging Conditions in Detecting Low Contrast Objects In Computed Tomography Phantom Images: in Comparison to 12 Radiologists

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 410
Author(s):  
Hae Young Kim ◽  
Kyeorye Lee ◽  
Won Chang ◽  
Youngjune Kim ◽  
Sungsoo Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Operating Characteristics ◽  
Low Contrast ◽  
Binary Responses ◽  
Deep Learning Algorithm ◽  
Receiver Operating Characteristics Curve ◽  
Post Hoc ◽  
Imaging Conditions

The performance of deep learning algorithm (DLA) to that of radiologists was compared in detecting low contrast objects in CT phantom images under various imaging conditions. For training, 10,000 images were created using American College of Radiology CT phantom as the background. In half of the images, objects of 3–20 mm size and 5–30 HU contrast difference were generated in random locations. Binary responses were used as the ground truth. For testing, 640 images of Catphan® phantom were used, half of which had objects of either 5 or 9 mm size with 10 HU contrast difference. Twelve radiologists evaluated the presence of objects on a five-point scale. The performances of the DLA and radiologists were compared across different imaging conditions in terms of area under receiver operating characteristics curve (AUC). Multi-reader multi-case AUC and Hanley and McNeil tests were used. We performed post-hoc analysis using bootstrapping and verified that the DLA is less affected by the changing imaging conditions. The AUC of DLA was consistently higher than those of the radiologists across different imaging conditions (p < 0.0001), and it was less affected by varying imaging conditions. The DLA outperformed the radiologists and showed more robust performance under varying imaging conditions.

scholarly journals Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 430 ◽  
Author(s):  
Michael G. Endres ◽  
Florian Hillen ◽  
Marios Salloumis ◽  
Ahmad R. Sedaghat ◽  
Stefan M. Niehues ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Ground Truth ◽  
True Positive Rate ◽  
Radiographic Images ◽  
Data Set ◽  
Radiographic Findings ◽  
Panoramic Radiographs ◽  
Deep Learning Algorithm ◽  
The Mean

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69 (±0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51 (±0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60 (±0.04), and an F1 score of 0.58 (±0.04) corresponding to a PPV of 0.67 (±0.05) and TPR of 0.51 (±0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs.

scholarly journals RADI-12. Deep learning for automatic detection and contouring of metastatic brain tumors in stereotactic radiosurgery: a retrospective analysis with an FDA-cleared software algorithm

2021 ◽  
Vol 3 (Supplement_3) ◽  
pp. iii20-iii20
Author(s):  
Jen-Yeu Wang ◽  
Navjot Sandhu ◽  
Maria Mendoza ◽  
Jhih-Yuan Lin ◽  
Yueh-Hung Cheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastases ◽  
Stereotactic Radiosurgery ◽  
Retrospective Analysis ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Slice Thickness ◽  
Pixel Resolution ◽  
Post Contrast ◽  
Deep Learning Algorithm

Abstract Introduction Artificial intelligence-based tools can significantly impact detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain is a deep learning algorithm, recently FDA-cleared, to assist in brain tumor contouring. In this study, we aimed to further validate this tool in patients treated with SRS for brain metastases at Stanford Cancer Center. Methods We included randomly selected patients with brain metastases treated with SRS from 2008 to 2020. Computed tomography (CT) and axial T1-weighted post-contrast magnetic resonance (MR) image data were extracted for each patient and uploaded to VBrain. Subsequent analyses compared the output contours from VBrain with the physician-defined contours used for SRS. A brain metastasis was considered “detected” when the VBrain “predicted” contours overlapped with the corresponding physician contours (“ground-truth” contours). We evaluated performance against ground-truth contours using the following metrics: lesion-wise Dice similarity coefficient (DSC), lesion-wise average Hausdorff distance (AVD), false positive count (FP), and lesion-wise sensitivity (%). Results We analyzed 60 patients with 321 intact brain metastases treated over 70 SRS courses. Resection cavities were excluded from the analysis. The median (range) tumor size was 132 mm3 (7 to 24,765). Input CT scan slice thickness was 1.250 mm, and median (range) pixel resolution was 0.547 mm (0.457 to 0.977). Input MR scan median (range) slice thickness was 1.000 mm (0.940 to 2.000), and median (range) pixel resolution was 0.469 mm (0.469 to 1.094). In assessing VBrain performance, we found mean lesion-wise DSC to be 0.70, mean lesion-wise AVD to be 9.40% of lesion size (0.805 mm), mean FP to be 0.657 tumors per case, and lesion-wise sensitivity to be 84.5%. Conclusion Retrospective analysis of our brain metastases cohort using a deep learning algorithm yielded promising results. Integration of VBrain into the clinical workflow can provide further clinical and research insights.

scholarly journals Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

2020 ◽  
Author(s):  
Prashant Sadashiv Gidde ◽  
Shyam Sunder Prasad ◽  
Ajay Pratap Singh ◽  
Nitin Bhatheja ◽  
Satyartha Prakash ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Image Interpretation ◽  
Ground Truth ◽  
Training Data ◽  
Screening Tools ◽  
X Rays ◽  
Rt Pcr ◽  
Middle Income ◽  
Deep Learning Algorithm

AbstractThe coronavirus disease of 2019 (COVID-19) pandemic exposed a limitation of artificial intelligence (AI) based medical image interpretation systems. Early in the pandemic, when need was greatest, the absence of sufficient training data prevented effective deep learning (DL) solutions. Even now, there is a need for Chest-X-ray (CxR) screening tools in low and middle income countries (LMIC), when RT-PCR is delayed, to exclude COVID-19 pneumonia (Cov-Pneum) requiring transfer to higher care. In absence of local LMIC data and poor portability of CxR DL algorithms, a new approach is needed. Axiomatically, it is faster to repurpose existing data than to generate new datasets. Here, we describe CovBaseAI, an explainable tool which uses an ensemble of three DL models and an expert decision system (EDS) for Cov-Pneum diagnosis, trained entirely on datasets from the pre-COVID-19 period. Portability, performance, and explainability of CovBaseAI was primarily validated on two independent datasets. First, 1401 randomly selected CxR from an Indian quarantine-center to assess effectiveness in excluding radiologic Cov-Pneum that may require higher care. Second, a curated dataset with 434 RT-PCR positive cases of varying levels of severity and 471 historical scans containing normal studies and non-COVID pathologies, to assess performance in advanced medical settings. CovBaseAI had accuracy of 87% with negative predictive value of 98% in the quarantine-center data for Cov-Pneum. However, sensitivity varied from 0.66 to 0.90 depending on whether RT-PCR or radiologist opinion was set as ground truth. This tool with explainability feature has better performance than publicly available algorithms trained on COVID-19 data but needs further improvement.

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