scholarly journals Hemopneumothorax Detection Through the Process of Artificial Evolution - A Feasibility Study

2020 ◽  
Author(s):  
Adir Sommer ◽  
Noy Mark ◽  
Rafi Gerasi ◽  
Linn Wagnert Avraham ◽  
Ruth Fan-Marko ◽  
...  
Keyword(s):  
Background Noise ◽  
Porcine Model ◽  
Ex Vivo ◽  
Tension Pneumothorax ◽  
Field Conditions ◽  
Clinical Impression ◽  
Artificial Evolution ◽  
Acoustic Signatures ◽  
Multi Objective Genetic Algorithm ◽  
Prehospital Diagnosis

Abstract Background : Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods : Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results : The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise. Conclusions : We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.

scholarly journals Hemopneumothorax Detection Through the Process of Artificial Evolution

2020 ◽  
Author(s):  
Adir Sommer ◽  
Noy Mark ◽  
Rafi Gerasi ◽  
Linn Wagnert Avraham ◽  
Ruth Fan-Marko ◽  
...  
Keyword(s):  
Background Noise ◽  
Porcine Model ◽  
Ex Vivo ◽  
Tension Pneumothorax ◽  
Field Conditions ◽  
Clinical Impression ◽  
Artificial Evolution ◽  
Acoustic Signatures ◽  
Multi Objective Genetic Algorithm ◽  
Prehospital Diagnosis

Abstract Background: Tension pneumothorax is a leading cause of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods: Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results: The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise.Conclusions: We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.

scholarly journals Hemopneumothorax Detection Through the Process of Artificial Evolution - A Feasibility Study

2020 ◽  
Author(s):  
Adir Sommer ◽  
Noy Mark ◽  
Rafi Gerasi ◽  
Linn Wagnert Avraham ◽  
Ruth Fan-Marko ◽  
...  
Keyword(s):  
Background Noise ◽  
Porcine Model ◽  
Ex Vivo ◽  
Tension Pneumothorax ◽  
Field Conditions ◽  
Clinical Impression ◽  
Artificial Evolution ◽  
Acoustic Signatures ◽  
Multi Objective Genetic Algorithm ◽  
Prehospital Diagnosis

Abstract Background: Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods: Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results: The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise.Conclusions: We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.

scholarly journals Hemopneumothorax detection through the process of artificial evolution - a feasibility study

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Adir Sommer ◽  
Noy Mark ◽  
Gavriel D. Kohlberg ◽  
Rafi Gerasi ◽  
Linn Wagnert Avraham ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Porcine Model ◽  
Ex Vivo ◽  
Tension Pneumothorax ◽  
Clinical Impression ◽  
Artificial Evolution ◽  
Acoustic Data ◽  
Acoustic Signatures ◽  
Multi Objective Genetic Algorithm ◽  
Prehospital Diagnosis

Abstract Background Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods Acoustic data was obtained with simultaneous use of two sensitive digital stethoscopes from the chest wall of an ex-vivo porcine model. Twelve second samples of acoustic data were obtained from the in-house assembled digital stethoscope system during mechanical ventilation. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air or saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results The in-house assembled dual digital stethoscope system and developed genetic algorithm achieved an accuracy, sensitivity and specificity ranging from 64 to 100%, 63 to 100%, and 63 to 100%, respectively, in classifying acoustic signal as associated with pneumothorax or hemothorax at fluid injection levels of 400 ml or more, and regardless of background noise. Conclusions We present a novel, objective device for rapid diagnosis of potentially lethal thoracic injuries. With further optimization, such a device could provide real-time detection and monitoring of pneumothorax and hemothorax in battlefield conditions.

Differential Outcomes for Long Term ex vivo Lung Perfusion in a Porcine Model - With or without Red Cells?

2015 ◽  
Vol 63 (S 01) ◽  
Author(s):  
W. Sommer ◽  
M. Avsar ◽  
J. Salman ◽  
C. Kühn ◽  
I. Tudorache ◽  
...  
Keyword(s):  
Porcine Model ◽  
Ex Vivo ◽  
Lung Perfusion ◽  
Red Cells ◽  
Ex Vivo Lung Perfusion ◽  
Differential Outcomes

scholarly journals Robotic-assisted cholecystectomy is superior to laparoscopic cholecystectomy in the initial training for surgical novices in an ex vivo porcine model: a randomized crossover study

2021 ◽  
Author(s):  
E. Willuth ◽  
S. F. Hardon ◽  
F. Lang ◽  
C. M. Haney ◽  
E. A. Felinska ◽  
...  
Keyword(s):  
Laparoscopic Cholecystectomy ◽  
Crossover Study ◽  
Porcine Model ◽  
Haptic Feedback ◽  
Ex Vivo ◽  
Operating Time ◽  
Surgical Performance ◽  
Robotic Assisted ◽  
Subjective Workload ◽  
Randomized Crossover Study

Abstract Background Robotic-assisted surgery (RAS) potentially reduces workload and shortens the surgical learning curve compared to conventional laparoscopy (CL). The present study aimed to compare robotic-assisted cholecystectomy (RAC) to laparoscopic cholecystectomy (LC) in the initial learning phase for novices. Methods In a randomized crossover study, medical students (n = 40) in their clinical years performed both LC and RAC on a cadaveric porcine model. After standardized instructions and basic skill training, group 1 started with RAC and then performed LC, while group 2 started with LC and then performed RAC. The primary endpoint was surgical performance measured with Objective Structured Assessment of Technical Skills (OSATS) score, secondary endpoints included operating time, complications (liver damage, gallbladder perforations, vessel damage), force applied to tissue, and subjective workload assessment. Results Surgical performance was better for RAC than for LC for total OSATS (RAC = 77.4 ± 7.9 vs. LC = 73.8 ± 9.4; p = 0.025, global OSATS (RAC = 27.2 ± 1.0 vs. LC = 26.5 ± 1.6; p = 0.012, and task specific OSATS score (RAC = 50.5 ± 7.5 vs. LC = 47.1 ± 8.5; p = 0.037). There were less complications with RAC than with LC (10 (25.6%) vs. 26 (65.0%), p = 0.006) but no difference in operating times (RAC = 77.0 ± 15.3 vs. LC = 75.5 ± 15.3 min; p = 0.517). Force applied to tissue was similar. Students found RAC less physical demanding and less frustrating than LC. Conclusions Novices performed their first cholecystectomies with better performance and less complications with RAS than with CL, while operating time showed no differences. Students perceived less subjective workload for RAS than for CL. Unlike our expectations, the lack of haptic feedback on the robotic system did not lead to higher force application during RAC than LC and did not increase tissue damage. These results show potential advantages for RAS over CL for surgical novices while performing their first RAC and LC using an ex vivo cadaveric porcine model. Registration number researchregistry6029 Graphic abstract

scholarly journals Development of a gel dedicated to gel immersion endoscopy

2021 ◽  
Vol 09 (06) ◽  
pp. E918-E924
Author(s):  
Tomonori Yano ◽  
Atsushi Ohata ◽  
Yuji Hiraki ◽  
Makoto Tanaka ◽  
Satoshi Shinozaki ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Porcine Model ◽  
Ex Vivo ◽  
In Vitro Experiments ◽  
Efficacy And Safety ◽  
Coagulative Necrosis ◽  
Novel Technique ◽  
In Vivo Experiments

Abstract Backgrounds and study aims Gel immersion endoscopy is a novel technique to secure the visual field during endoscopy. The aim of this study was to develop a dedicated gel for this technique. Methods To identify appropriate viscoelasticity and electrical conductivity, various gels were examined. Based on these results, the dedicated gel “OPF-203” was developed. Efficacy and safety of OPF-203 were evaluated in a porcine model. Results  In vitro experiments showed that a viscosity of 230 to 1900 mPa·s, loss tangent (tanδ) ≤ 0.6, and hardness of 240 to 540 N/cm2 were suitable. Ex vivo experiments showed electrical conductivity ≤ 220 μS/cm is appropriate. In vivo experiments using gastrointestinal bleeding showed that OPF-203 provided clear visualization compared to water. After electrocoagulation of gastric mucosa in OPF-203, severe coagulative necrosis was not observed in the muscularis but limited to the mucosa. Conclusions OPF-203 is useful for gel immersion endoscopy.

scholarly journals Lowering Perfusate Temperature From 37°C to 32°C Diminishes Function in a Porcine Model of Ex Vivo Kidney Perfusion

2017 ◽  
Vol 3 (3) ◽  
pp. e140 ◽  
Author(s):  
Thomas D. Adams ◽  
Meeta Patel ◽  
Sarah A. Hosgood ◽  
Michael L. Nicholson
Keyword(s):  
Porcine Model ◽  
Ex Vivo ◽  
Kidney Perfusion

Description of a new, endoscopic technique to remove the over-the-scope-clip in an ex vivo porcine model (with video)

2012 ◽  
Vol 76 (5) ◽  
pp. 1009-1013 ◽  
Author(s):  
Helmut Neumann ◽  
Hiwot Diebel ◽  
Klaus Mönkemüller ◽  
Andreas Nägel ◽  
Dane Wildner ◽  
...  
Keyword(s):  
Porcine Model ◽  
Ex Vivo ◽  
Endoscopic Technique ◽  
Over The Scope Clip

Localized Acoustic-Event Measurement Probe: Connector Confirmation Utilizing Acoustic Signatures

2020 ◽  
Author(s):  
Brian Skoglind ◽  
Travis Roberts ◽  
Sourabh Karmakar ◽  
Cameron Turner ◽  
Laine Mears
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Consumer Products ◽  
Noise Mitigation ◽  
Acoustic Event ◽  
Electrical Connection ◽  
Acoustic Signatures ◽  
Electrical Connector ◽  
Electrical Connections

Abstract Electrical connections in consumer products are typically made manually rather than through automated assembly systems due to the high variety of connector types and connector positions, and the soft flexible nature of their structures. Manual connections are prone to failure through missed or improper connections in the assembly process and can lead to unexpected downtime and expensive rework. Past approaches for registering connection success such as vision verification or Augmented Reality have shown limited ability to verify correct connection state. However, the feasibility of an acoustic-based verification system for electrical connector confirmation has not been extensively researched. One of the major problems preventing acoustic based verification in a manufacturing or assembly environment is the typically low signal to noise ratio (SNR) between the sound of an electrical connection and the diverse soundscape of the plant. In this study, a physical means of background noise mitigation and signature amplification are investigated in order to increase the SNR between the electrical connection and the plant soundscape in order to improve detection. The concept is that an increase in the SNR will lead to an improvement in the accuracy and robustness of an acoustic event detection and classification system. Digital filtering has been used in the past to deal with low SNRs, however, it runs the risk of filtering out potential important features for classification. A sensor platform is designed to filter out and reduce background noise from the plant without effecting the raw acoustic signal of the electrical connection, and an automated detection algorithm is presented. The solution is over 75% effective at detecting and classifying connections.

Sign in / Sign up

Export Citation Format

Share Document