Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

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Development and Prospective Validation of a Transparent Deep Learning Algorithm for Predicting Need for Mechanical Ventilation

10.1101/2020.05.30.20118109 ◽

2020 ◽

Cited By ~ 1

Author(s):

Supreeth P. Shashikumar ◽

Gabriel Wardi ◽

Paulina Paul ◽

Morgan Carlile ◽

...

Keyword(s):

Mechanical Ventilation ◽

Deep Learning ◽

Learning Algorithm ◽

Characteristic Curve ◽

External Validation ◽

Improve Patient Care ◽

Hospitalized Patients ◽

Clinical Criteria ◽

Prediction Horizon ◽

Deep Learning Algorithm

ABSTRACTIMPORTANCEObjective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is of great importance and may aid in delivering timely treatment.OBJECTIVETo develop, externally validate and prospectively test a transparent deep learning algorithm for predicting 24 hours in advance the need for mechanical ventilation in hospitalized patients and those with COVID-19.DESIGNObservational cohort studySETTINGTwo academic medical centers from January 01, 2016 to December 31, 2019 (Retrospective cohorts) and February 10, 2020 to May 4, 2020 (Prospective cohorts).PARTICIPANTSOver 31,000 admissions to the intensive care units (ICUs) at two hospitals. Additionally, 777 patients with COVID-19 patients were used for prospective validation. Patients who were placed on mechanical ventilation within four hours of their admission were excluded.MAIN OUTCOME(S) and MEASURE(S)Electronic health record (EHR) data were extracted on an hourly basis, and a set of 40 features were calculated and passed to an interpretable deep-learning algorithm to predict the future need for mechanical ventilation 24 hours in advance. Additionally, commonly used clinical criteria (based on heart rate, oxygen saturation, respiratory rate, FiO2 and pH) was used to assess future need for mechanical ventilation. Performance of the algorithms were evaluated using the area under receiver-operating characteristic curve (AUC), sensitivity, specificity and positive predictive value.RESULTSAfter applying exclusion criteria, the external validation cohort included 3,888 general ICU and 402 COVID-19 patients. The performance of the model (AUC) with a 24-hour prediction horizon at the validation site was 0.882 for the general ICU population and 0.918 for patients with COVID-19. In comparison, commonly used clinical criteria and the ROX score achieved AUCs in the range of 0.773 – 0.782 and 0.768 – 0.810 for the general ICU population and patients with COVID-19, respectively.CONCLUSIONS and RELEVANCEA generalizable and transparent deep-learning algorithm improves on traditional clinical criteria to predict the need for mechanical ventilation in hospitalized patients, including those with COVID-19. Such an algorithm may help clinicians with optimizing timing of tracheal intubation, better allocation of mechanical ventilation resources and staff, and improve patient care.

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Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database

Diagnostics ◽

10.3390/diagnostics11081385 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1385

Author(s):

Marc Baget-Bernaldiz ◽

Romero-Aroca Pedro ◽

Esther Santos-Blanco ◽

Raul Navarro-Gil ◽

Aida Valls ◽

...

Keyword(s):

Deep Learning ◽

Predictive Value ◽

Operating Characteristic ◽

Learning Algorithm ◽

Characteristic Curve ◽

Retinal Images ◽

Deep Learning Algorithm ◽

Type 2 Dm ◽

Diabetic Population

Background: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). Results: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. Conclusions: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.

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ID: 3493047 DEVELOPMENT OF A DEEP LEARNING ALGORITHM TO EVALUATE SMALL BOWEL PREPARATION QUALITY

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2021.03.892 ◽

2021 ◽

Vol 93 (6) ◽

pp. AB351

Author(s):

Ji Hyung Nam ◽

Dong Jun Oh ◽

Junseok Park ◽

Hyun Joo Song ◽

Yun Jeong Lim

Keyword(s):

Deep Learning ◽

Small Bowel ◽

Bowel Preparation ◽

Learning Algorithm ◽

Deep Learning Algorithm

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Deep learning in the field of disease diagnosis

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.25.12364 ◽

2018 ◽

Vol 7 (2.25) ◽

pp. 37

Author(s):

K S. Harish Kumar ◽

Dijo Micheal Jerald ◽

A Emmanuel

Keyword(s):

Deep Learning ◽

Medical Errors ◽

Learning Algorithm ◽

Disease Diagnosis ◽

Good Treatment ◽

Test Results ◽

Medical Field ◽

Deep Learning Algorithm ◽

Medical Reports ◽

Severity Of The Disease

A good treatment is dependent on the accuracy of the diagnosis. The cure for the disease starts with the process of diagnosis. All these years, the grade and standard of the medical field has been increasing exponentially, yet there has been no significant downfall in the rate of unintentional medical errors. These errors can be avoided using Deep learning algorithm to predict the disease. The Deep Learning algorithm scans analyses and compares the patient's report with its dataset and predicts the nature and severity of the disease. The test results from the patient’s report are extracted by using PDF processing. More the medical reports analyzed, more will be the intelligence gained by the algorithm. This will be of great assistance to the doctors as they can interpret their diagnosis with the results predicted by the algorithm.

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Deep Learning for Identification of Acute Illness and Facial Cues of Illness

Frontiers in Medicine ◽

10.3389/fmed.2021.661309 ◽

2021 ◽

Vol 8 ◽

Author(s):

Castela Forte ◽

Andrei Voinea ◽

Malina Chichirau ◽

Galiya Yeshmagambetova ◽

Lea M. Albrecht ◽

...

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Learning Algorithm ◽

External Validation ◽

Facial Feature ◽

Acute Illness ◽

Facial Features ◽

Genetic Syndromes ◽

Coronary Syndrome ◽

Deep Learning Algorithm

Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt.Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals.Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS).Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3–33.1% for the skin model) to 89.4% (66.9–98.7%, for the nose model). Specificity ranged from 42.1% (20.3–66.5%) for the nose model and 94.7% (73.9–99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62–0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35–100.00%) and specificity of 42.11% (20.25–66.50%).Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness.

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Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning

Nature Biomedical Engineering ◽

10.1038/s41551-020-00633-5 ◽

2020 ◽

Vol 4 (12) ◽

pp. 1197-1207

Author(s):

Wanshan Ning ◽

Shijun Lei ◽

Jingjing Yang ◽

Yukun Cao ◽

Peiran Jiang ◽

...

Keyword(s):

Deep Learning ◽

Clinical Decision Making ◽

Learning Algorithm ◽

Characteristic Curve ◽

Clinical Status ◽

Clinical Decision ◽

Viral Disease ◽

Deep Learning Algorithm ◽

Blood And Urine Samples ◽

Ct Slices

AbstractData from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

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Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs

European Respiratory Journal ◽

10.1183/13993003.03061-2020 ◽

2020 ◽

pp. 2003061

Author(s):

Ju Gang Nam ◽

Minchul Kim ◽

Jongchan Park ◽

Eui Jin Hwang ◽

Jong Hyuk Lee ◽

...

Keyword(s):

Emergency Department ◽

Deep Learning ◽

Learning Algorithm ◽

External Validation ◽

Chest Radiographs ◽

Reading Tests ◽

Deep Learning Algorithm ◽

The Mean ◽

Emergency Department Patients ◽

Development And Validation

We aimed to develop a deep-learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs and to evaluate its impact in diagnostic accuracy, timeliness of reporting, and workflow efficacy.DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiologic abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule/mass, consolidation, pleural effusion, linear atelectasis, fibrosis, calcification, and cardiomegaly). For external validation, the performance of DLAD-10 on a same-day CT-confirmed dataset (normal:abnormal, 53:147) and an open-source dataset (PadChest; normal:abnormal, 339:334) was compared to that of three radiologists. Separate simulated reading tests were conducted on another dataset adjusted to real-world disease prevalence in the emergency department, consisting of four critical, 52 urgent, and 146 non-urgent cases. Six radiologists participated in the simulated reading sessions with and without DLAD-10.DLAD-10 exhibited areas under the receiver-operating characteristic curves (AUROCs) of 0.895–1.00 in the CT-confirmed dataset and 0.913–0.997 in the PadChest dataset. DLAD-10 correctly classified significantly more critical abnormalities (95.0% [57/60]) than pooled radiologists (84.4% [152/180]; p=0.01). In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8% [17/24] versus 29.2% [7/24]; p=0.006) and urgent (82.7% [258/312] versus 78.2% [244/312]; p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean time-to-report critical and urgent radiographs (640.5±466.3 versus 3371.0±1352.5 s and 1840.3±1141.1 versus 2127.1±1468.2, respectively; p-values<0.01) and reduced the mean interpretation time (20.5±22.8 versus 23.5±23.7 s; p<0.001).DLAD-10 showed excellent performance, improving radiologists' performance and shortening the reporting time for critical and urgent cases.

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Automated acquisition of explainable knowledge from unannotated histopathology images

Nature Communications ◽

10.1038/s41467-019-13647-8 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 12

Author(s):

Yoichiro Yamamoto ◽

Toyonori Tsuzuki ◽

Jun Akatsuka ◽

Masao Ueki ◽

Hiromu Morikawa ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

External Validation ◽

Cancer Recurrence ◽

Deep Learning Algorithm ◽

Image Patches ◽

Prostate Cancer Recurrence ◽

Histopathology Images ◽

Prognostic Prediction ◽

Learning Analysis

AbstractDeep learning algorithms have been successfully used in medical image classification. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Here we show that deep learning algorithm enables automated acquisition of explainable features from diagnostic annotation-free histopathology images. We compare the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by expert pathologists using established criteria on 13,188 whole-mount pathology images consisting of over 86 billion image patches. Our method not only reveals findings established by humans but also features that have not been recognized, showing higher accuracy than human in prognostic prediction. Combining both our algorithm-generated features and human-established criteria predicts the recurrence more accurately than using either method alone. We confirm robustness of our method using external validation datasets including 2276 pathology images. This study opens up fields of machine learning analysis for discovering uncharted knowledge.

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A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

Scientific Reports ◽

10.1038/s41598-021-92172-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yong-Soo Baek ◽

Sang-Chul Lee ◽

Wonik Choi ◽

Dae-Hyeok Kim

Keyword(s):

Atrial Fibrillation ◽

Deep Learning ◽

Sinus Rhythm ◽

Learning Algorithm ◽

Normal Sinus Rhythm ◽

Characteristic Curve ◽

External Validation ◽

Digital Data ◽

Internal Validation ◽

Optimal Interval

AbstractAtrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

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

PLoS ONE ◽

10.1371/journal.pone.0246472 ◽

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.

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