296 Development of an Artificial Intelligence Deep Learning Algorithm That Utilizes IVC Collapse to Predict Fluid Responsiveness

Abstract Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

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Selective Disinfection Based on Directional Ultraviolet Irradiation and Artificial Intelligence

Electronics ◽

10.3390/electronics10202557 ◽

2021 ◽

Vol 10 (20) ◽

pp. 2557

Author(s):

Ben Zierdt ◽

Taichu Shi ◽

Thomas DeGroat ◽

Sam Furman ◽

Nicholas Papas ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Ultraviolet Radiation ◽

Ultraviolet Irradiation ◽

Human Exposure ◽

Large Scale ◽

Learning Algorithm ◽

Ultraviolet Disinfection ◽

Time Efficiency ◽

Deep Learning Algorithm

Ultraviolet disinfection has been proven to be effective for surface sanitation. Traditional ultraviolet disinfection systems generate omnidirectional radiation, which introduces safety concerns regarding human exposure. Large scale disinfection must be performed without humans present, which limits the time efficiency of disinfection. We propose and experimentally demonstrate a targeted ultraviolet disinfection system using a combination of robotics, lasers, and deep learning. The system uses a laser-galvo and a camera mounted on a two-axis gimbal running a custom deep learning algorithm. This allows ultraviolet radiation to be applied to any surface in the room where it is mounted, and the algorithm ensures that the laser targets the desired surfaces avoids others such as humans. Both the laser-galvo and the deep learning algorithm were tested for targeted disinfection.

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Using Artificial Intelligence Deep Learning Algorithm to Evaluate and Guide the Quality Improvement Work in Neurosurgical Practice

International Journal of Surgery ◽

10.1016/j.ijsu.2017.08.318 ◽

2017 ◽

Vol 47 ◽

pp. S61 ◽

Cited By ~ 1

Author(s):

M. Brzezicki ◽

M. Kobetic

Keyword(s):

Artificial Intelligence ◽

Quality Improvement ◽

Deep Learning ◽

Learning Algorithm ◽

Deep Learning Algorithm

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Artificial Intelligence in Steam Cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction

Engineering ◽

10.1016/j.eng.2019.02.013 ◽

2019 ◽

Vol 5 (6) ◽

pp. 1027-1040 ◽

Cited By ~ 3

Author(s):

Pieter P. Plehiers ◽

Steffen H. Symoens ◽

Ismaël Amghizar ◽

Guy B. Marin ◽

Christian V. Stevens ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Learning Algorithm ◽

Deep Learning Algorithm ◽

Steam Cracking

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Development of an Underground Tunnels Detection Algorithm for Electrical Resistivity Tomography Based on Deep Learning

Applied Sciences ◽

10.3390/app12020639 ◽

2022 ◽

Vol 12 (2) ◽

pp. 639

Author(s):

Yin-Chun Hung ◽

Yu-Xiang Zhao ◽

Wei-Chen Hung

Keyword(s):

Artificial Intelligence ◽

Electrical Resistivity ◽

Deep Learning ◽

Electrical Resistivity Tomography ◽

Learning Algorithm ◽

Detection Algorithm ◽

Image Resolution ◽

Resistivity Tomography ◽

Military Installations ◽

Deep Learning Algorithm

Kinmen Island was in a state of combat readiness during the 1950s–1980s. It opened for tourism in 1992, when all troops withdrew from the island. Most military installations, such as bunkers, anti airborne piles, and underground tunnels, became deserted and disordered. The entries to numerous underground bunkers are closed or covered with weeds, creating dangerous spaces on the island. This study evaluates the feasibility of using Electrical Resistivity Tomography (ERT) to detect and discuss the location, size, and depth of underground tunnels. In order to discuss the reliability of the 2D-ERT result, this study built a numerical model to validate the correctness of in situ measured data. In addition, this study employed the artificial intelligence deep learning technique for reprocessing and predicting the ERT image and discussed using an artificial intelligence deep learning algorithm to enhance the image resolution and interpretation. A total of three 2D-ERT survey lines were implemented in this study. The results indicate that the three survey lines clearly show the tunnel location and shape. The numerical simulation results also indicate that using 2D-ERT to survey underground tunnels is highly feasible. Moreover, according to a series of studies in Multilayer Perceptron of deep learning, using deep learning can clearly show the tunnel location and path and effectively enhance the interpretation ability and resolution for 2D-ERT measurement results.

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Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18073839 ◽

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.

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Transfer Learning via Artificial Intelligence for Guiding Implant Placement in the Posterior Mandible: an in vitro Study

10.21203/rs.3.rs-986672/v1 ◽

2021 ◽

Author(s):

Yun Liu ◽

Zhi-cong Chen ◽

Chun-ho Chu ◽

Fei-Long Deng

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Learning Algorithm ◽

In Vitro Study ◽

Angular Deviation ◽

Single Shot ◽

Implant Position ◽

Deep Learning Algorithm ◽

Group A

Abstract Background: To explore the capacity of a single shot multibox detector (SSD) and Voxel-to-voxel prediction network for pose estimation (V2V-PoseNet) based artificial intelligence (AI) system in automatically designing implant plan. Methods: 2500 and 67 cases were used to develop and pre-train the AI system. After that, 12 patients who missed the mandibular left first molars were selected to test the capacity of the AI in automatically designing implant plan. There were three algorithms-based implant positions. They are Group A, B and C (8, 9 and 10 points dependent implant position, respectively). The AI system was then used to detect the characteristic annotators and determine the implant position. For every group, the actual implant position was compared with the algorithm-determined ideal position. And global, angular, depth and lateral deviation were calculate. One-way ANOVA followed by Tukey’s test was performed for statistical comparisons. The significance value was set at P< 0.05. Results: Group C represented the least coronal (0.6638±0.2651, range: 0.2060 to 1.109 mm) and apical (1.157±0.3350, range: 0.5840 to 1.654 mm) deviation, the same trend was observed in the angular deviation (5.307 ±2.891°, range: 2.049 to 10.90°), and the results are similar with the traditional statistic guide.Conclusion: It can be concluded that the AI system has the capacity of deep learning. And as more characteristic annotators be involved in the algorithm, the AI system can figure out the anatomy of the object region better, then generate the ideal implant plan via deep learning algorithm.

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