Using Deep Learning to Improve Nonsystematic Viewing of Breast Cancer on MRI

2021 ◽  
Author(s):  
Sarah Eskreis-Winkler ◽  
Natsuko Onishi ◽  
Katja Pinker ◽  
Jeffrey S Reiner ◽  
Jennifer Kaplan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Learning Algorithm ◽  
Breast Mri ◽  
Learning System ◽  
Tumor Board ◽  
Unilateral Breast Cancer ◽  
Deep Learning Algorithm ◽  
Picture Archiving And Communication

Abstract Objective To investigate the feasibility of using deep learning to identify tumor-containing axial slices on breast MRI images. Methods This IRB–approved retrospective study included consecutive patients with operable invasive breast cancer undergoing pretreatment breast MRI between January 1, 2014, and December 31, 2017. Axial tumor-containing slices from the first postcontrast phase were extracted. Each axial image was subdivided into two subimages: one of the ipsilateral cancer-containing breast and one of the contralateral healthy breast. Cases were randomly divided into training, validation, and testing sets. A convolutional neural network was trained to classify subimages into “cancer” and “no cancer” categories. Accuracy, sensitivity, and specificity of the classification system were determined using pathology as the reference standard. A two-reader study was performed to measure the time savings of the deep learning algorithm using descriptive statistics. Results Two hundred and seventy-three patients with unilateral breast cancer met study criteria. On the held-out test set, accuracy of the deep learning system for tumor detection was 92.8% (648/706; 95% confidence interval: 89.7%–93.8%). Sensitivity and specificity were 89.5% and 94.3%, respectively. Readers spent 3 to 45 seconds to scroll to the tumor-containing slices without use of the deep learning algorithm. Conclusion In breast MR exams containing breast cancer, deep learning can be used to identify the tumor-containing slices. This technology may be integrated into the picture archiving and communication system to bypass scrolling when viewing stacked images, which can be helpful during nonsystematic image viewing, such as during interdisciplinary tumor board meetings.

scholarly journals Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study

2021 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

External Validation Study On The Value of Deep Learning Algorithm For The Prediction of Hematoma Expansion From Noncontrast CT Scans

2021 ◽  
Author(s):  
Dong Chuang Guo ◽  
Jun Gu ◽  
Jian He ◽  
Hai Rui Chu ◽  
Na Dong ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Ct Scan ◽  
Sensitivity And Specificity ◽  
Gold Standard ◽  
Learning Algorithm ◽  
External Validation ◽  
Hematoma Expansion ◽  
Fast Method ◽  
Deep Learning Algorithm

Abstract Background: Hematoma expansion is an independent predictor of patient outcome and mortality. The early diagnosis of hematoma expansion is crucial for selecting clinical treatment options This study aims to explore the value of a deep learning algorithm for the prediction of hematoma expansion from noncontrast Computed tomography（NCCT） scan through external validation.Methods: 102 NCCT images of Hypertensive intracerebral hemorrhage (HICH) patients diagnosed in our hospital were retrospectively reviewed. The initial Computed tomography (CT) scan images were evaluated by a commercial Artificial intelligence (AI) software using deep learning algorithm and radiologists respectively to predict hematoma expansion and the corresponding sensitivity and specificity of the two groups were calculated and compared, Pair-wise comparisons were conducted among gold standard hematoma expansion diagnosis time, AI software diagnosis time and doctors’ reading time.Results: Among 102 HICH patients, The sensitivity, specificity and accuracy of predicting hematoma expansion in the AI group were higher than those in the doctor group（80.0% vs 66.7%,73.6% vs 58.3%,75.5% vs 60.8%）,with statistically significant difference (p<0.05).The AI diagnosis time (2.8 ± 0.3s) and the doctors’ diagnosis time (11.7 ± 0.3s) were both significantly shorter than the gold standard diagnosis time (14.5 ± 8.8h) (p <0.05), AI diagnosis time was significantly shorter than that of doctors (p<0.05).Conclusions: Deep learning algorithm could effectively predict hematoma expansion at an early stage from the initial CT scan images of HICH patients after onset with high sensitivity and specificity and greatly shortened diagnosis time, which provides a new, accurate, easy-to-use and fast method for the early prediction of hematoma expansion.

Deep learning algorithm performance in mammography screening: A systematic review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13553-e13553
Author(s):  
Rosimeire Aparecida Roela ◽  
Gabriel Vansuita Valente ◽  
Carlos Shimizu ◽  
Rossana Veronica Mendoza Lopez ◽  
Tatiana Cardoso de Mello Tucunduva ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Mammography Screening ◽  
Learning Algorithm ◽  
Data Extraction ◽  
Meta Analysis ◽  
Paper Analysis

e13553 Background: Mammography interpretation presents some challenges however, better technological approaches have allowed increased accuracy in cancer diagnosis and nowadays, radiologists sensitivity and specificity for mammography screening vary from 84.5 to 90.6 and 89.7 to 92.0%, respectively. Since its introduction in breast image analysis, artificial intelligence (AI) has rapidly improved and deep learning methods are gaining relevance as a companion tool to radiologists. Thus, the aim of this systematic review and meta analysis was to evaluate the sensitivity and specificity of AI deep learning algorithms and radiologists for breast cancer detection through mammography. Methods: A systematic review was performed using PubMed and the words: deep learning or convolutional neural network and mammography or mammogram, from January 2015 to October 2020. All titles and abstracts were doubly checked; duplicate studies and studies in languages other than English were excluded. The remaining complete studies were doubly assessed and those with specificity and sensibility information had data collected. For the meta analysis, studies reporting specificity, sensitivity and confidence intervals were selected. Heterogeneity measures were calculated using Cochran Q test (chi-square test) and the I2 (percentage of variation). Sensitivity and specificity and 95% confidence intervals (CI) values were calculated, using Stata/MP 14.0 for Windows. Results: Among 223 studies, 66 were selected for full paper analysis and 24 were selected for data extraction. Subsequently, only papers evaluating sensitivity, especificity, CI and/or AUC were analyzed. Eleven studies compared AUC using AI with another method and for these studies, a differential AUC was calculated, however no differences were observed: AI vs Reader (n = 3; p = 0.109); AI vs AI (n = 5; p = 0.225); AI vs AI + reader (n = 2; p = 0.180); AI + Reader vs reader (n = 2; p = 0.655); AI vs reader (n > 1) (n = 3; p = 0.102). Some studies had more than one comparison. A meta analysis was performed to evaluate sensitivity and specificity of the methods. Five studies were included in this analysis and a great heterogeneity among them was observed. There were studies evaluating more than one AI algorithm and studies comparing AI with readers alone or in combination with AI. Sensitivity for AI; AI + reader; reader alone, were 76.08; 84.02; 80.91, respectively. Specificity for AI; AI + reader; reader alone, were 96.62; 85.67; 84.89, respectively. Results are shown in the table. Conclusions: Although recent improvements in AI algorithms for breast cancer screening, a delta AUC between comparisons of AI algorithms and readers was not observed.[Table: see text]

scholarly journals Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
B. Athira ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

AbstractThe widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

scholarly journals Screening Referable Diabetic Retinopathy Using a Semi-automated Deep Learning Algorithm Assisted Approach

2021 ◽  
Vol 8 ◽  
Author(s):  
Yueye Wang ◽  
Danli Shi ◽  
Zachary Tan ◽  
Yong Niu ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Economic Cost ◽  
Positive Image ◽  
Deep Learning Algorithm ◽  
Image Datasets

Purpose: To assess the accuracy and efficacy of a semi-automated deep learning algorithm (DLA) assisted approach to detect vision-threatening diabetic retinopathy (DR).Methods: We developed a two-step semi-automated DLA-assisted approach to grade fundus photographs for vision-threatening referable DR. Study images were obtained from the Lingtou Cohort Study, and captured at participant enrollment in 2009–2010 (“baseline images”) and annual follow-up between 2011 and 2017. To begin, a validated DLA automatically graded baseline images for referable DR and classified them as positive, negative, or ungradable. Following, each positive image, all other available images from patients who had a positive image, and a 5% random sample of all negative images were selected and regraded by trained human graders. A reference standard diagnosis was assigned once all graders achieved consistent grading outcomes or with a senior ophthalmologist's final diagnosis. The semi-automated DLA assisted approach combined initial DLA screening and subsequent human grading for images identified as high-risk. This approach was further validated within the follow-up image datasets and its time and economic costs evaluated against fully human grading.Results: For evaluation of baseline images, a total of 33,115 images were included and automatically graded by the DLA. 2,604 images (480 positive results, 624 available other images from participants with a positive result, and 1500 random negative samples) were selected and regraded by graders. The DLA achieved an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.953, 0.970, 0.879, and 88.6%, respectively. In further validation within the follow-up image datasets, a total of 88,363 images were graded using this semi-automated approach and human grading was performed on 8975 selected images. The DLA achieved an AUC, sensitivity, and specificity of 0.914, 0.852, 0.853, respectively. Compared against fully human grading, the semi-automated DLA-assisted approach achieved an estimated 75.6% time and 90.1% economic cost saving.Conclusions: The DLA described in this study was able to achieve high accuracy, sensitivity, and specificity in grading fundus images for referable DR. Validated against long-term follow-up datasets, a semi-automated DLA-assisted approach was able to accurately identify suspect cases, and minimize misdiagnosis whilst balancing safety, time, and economic cost.

scholarly journals Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study

2020 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

scholarly journals Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography

2021 ◽  
Vol 18 (7) ◽  
pp. 3839
Author(s):  
Da-Wei Chang ◽  
Chin-Sheng Lin ◽  
Tien-Ping Tsao ◽  
Chia-Cheng Lee ◽  
Jiann-Torng Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Delayed Diagnosis ◽  
Learning System ◽  
Digoxin Toxicity ◽  
Therapeutic Window ◽  
Deep Learning Algorithm ◽  
Stratified Analysis

Although digoxin is important in heart rate control, the utilization of digoxin is declining due to its narrow therapeutic window. Misdiagnosis or delayed diagnosis of digoxin toxicity is common due to the lack of awareness and the time-consuming laboratory work that is involved. Electrocardiography (ECG) may be able to detect potential digoxin toxicity based on characteristic presentations. Our study attempted to develop a deep learning model to detect digoxin toxicity based on ECG manifestations. This study included 61 ECGs from patients with digoxin toxicity and 177,066 ECGs from patients in the emergency room from November 2011 to February 2019. The deep learning algorithm was trained using approximately 80% of ECGs. The other 20% of ECGs were used to validate the performance of the Artificial Intelligence (AI) system and to conduct a human-machine competition. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of ECG interpretation between humans and our deep learning system. The AUCs of our deep learning system for identifying digoxin toxicity were 0.912 and 0.929 in the validation cohort and the human-machine competition, respectively, which reached 84.6% of sensitivity and 94.6% of specificity. Interestingly, the deep learning system using only lead I (AUC = 0.960) was not worse than using complete 12 leads (0.912). Stratified analysis showed that our deep learning system was more applicable to patients with heart failure (HF) and without atrial fibrillation (AF) than those without HF and with AF. Our ECG-based deep learning system provides a high-accuracy, economical, rapid, and accessible way to detect digoxin toxicity, which can be applied as a promising decision supportive system for diagnosing digoxin toxicity in clinical practice.

scholarly journals Early-Stage Detection of Cancer in Breast Using Artificial Intelligence

2021 ◽  
Vol 11 (2) ◽  
pp. 2016-2028
Author(s):  
M.N. Vimal Kumar ◽  
S. Aakash Ram ◽  
C. Shobana Nageswari ◽  
C. Raveena ◽  
S. Rajan
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Early Stage ◽  
Deep Learning Algorithm ◽  
Common Cancer ◽  
Abnormal Cells ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

One of the deadly diseases among humans is Cancer, which occurs almost anywhere in the human body. Cancer is caused by the cells that spread into the surrounding tissues by dividing itself uncontrollably. Breast Cancer is the most common cancer among women. Early detection and diagnosis of breast cancer are treatable and curable. Many women have no symptoms for this cancer at an early stage. The abnormal cells in the breast will risk for the development of breast cancer. So, it is important for women to regularly examine their breast. Technologies can be utilized in a smarter way with Artificial Intelligence techniques to assist the women during their examination of the breast at their living place to avoid the risk of breast cancer. The main aim is to develop a lowcost self-examining device for the detection of breast cancer and abnormality in the breast using an efficient optical method, Deep-learning algorithm and Internet of Things.

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