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Everything you need to know about Computed Tomography (CT) & CT Scanning

Deep Learning: Deep Learning and the Gi Tract Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and the GI Tract

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  • Purpose: The aim of the study was to develop a prediction model for closed-loop small bowel obstruction integrating computed tomography (CT) and clinical findings.
    Conclusions: A random forest model found clinical factors including prior surgery, age, lactate, and imaging factors including whirl sign, fecalization, and U/C-shaped bowel configuration are helpful in improving the prediction of CLSBO. Individual CT findings in CLSBO had either high sensitivity or specificity, suggesting that accurate diagnosis requires systematic assessment of all CT signs.
    Machine Learning Based Prediction Model for Closed-Loop Small Bowel Obstruction Using Computed Tomography and Clinical Findings
    Goyal, Riya et al
    J Comput Assist Tomography: 3/4 2022 - Volume 46 - Issue 2 - p 169-174
  • Results: Surgery confirmed CLSBO in 185 of 223 patients with clinically suspected CLSBO. Age greater than 52 years showed 2.82 (95% confidence interval = 1.13–4.77) times higher risk for CLSBO (P = 0.021). Sensitivity/specificity of CT findings included proximal dilatation (97/5%), distal collapse (96/2%), mesenteric edema (94/5%), pneumatosis (1/100%), free air (1/98%), and portal venous gas (0/100%). The random forest model combining imaging/clinical findings yielded an area under receiver operating curve of 0.73 (95% confidence interval = 0.58–0.94), sensitivity of 0.72 (0.55–0.85), specificity of 0.8 (0.28–0.99), and accuracy of 0.73 (0.57–0.85). Prior surgery, age, lactate, whirl sign, U/C-shaped bowel configuration, and fecalization were the most important variables in predicting CLSBO.
    Machine Learning Based Prediction Model for Closed-Loop Small Bowel Obstruction Using Computed Tomography and Clinical Findings
    Goyal, Riya et al
    J Comput Assist Tomography: 3/4 2022 - Volume 46 - Issue 2 - p 169-174
  • “Major advances in medical AI have had a tremendous impact at two main levels: (1) image recognition and (2) big data analysis. AI can detect very small changes that are difficult for humans to perceive. For example, AI can detect lung cancer up to a year before a physician [3], and AI can correctly diagnose skin cancer with superior diagnostic performance compared to that of a physician [4]. In addition, AI can reach the desired output within seconds and with more “consistent” performance. Doctors may have “inconsistent” performance due to insufficient training or exhaustion from busy clinical demands. A visual assessment by imaging physicians is qualitative, subjective, and prone to errors, and subject to intra-observer and inter-observer variability. AI may have better performance than physicians in some cases [5], and it has great promise to reduce clinician workload and the cost of medical care. However, it is necessary for clinicians to verify the output from AI for patient care.”
    A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology
    Akihiko Oka  , Norihisa Ishimura and Shunji Ishihara  
    Diagnostics 2021, 11, 1719. https://doi.org/10.3390/diagnostics11091719 
  • “Major advances in medical AI have had a tremendous impact at two main levels: (1) image recognition and (2) big data analysis. AI can detect very small changes that are difficult for humans to perceive. For example, AI can detect lung cancer up to a year before a physician [3], and AI can correctly diagnose skin cancer with superior diagnostic performance compared to that of a physician [4]. In addition, AI can reach the desired output within seconds and with more “consistent” performance. Doctors may have “inconsistent” performance due to insufficient training or exhaustion from busy clinical demands. A visual assessment by imaging physicians is qualitative, subjective, and prone to errors, and subject to intra-observer and inter-observer variability. AI may have better performance than physicians in some cases [5], and it has great promise to reduce clinician workload and the cost of medical care. However, it is necessary for clinicians to verify the output from AI for patient care.”
    A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology
    Akihiko Oka  , Norihisa Ishimura and Shunji Ishihara  
    Diagnostics 2021, 11, 1719. https://doi.org/10.3390/diagnostics11091719 

  • A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology
    Akihiko Oka  , Norihisa Ishimura and Shunji Ishihara  
    Diagnostics 2021, 11, 1719. https://doi.org/10.3390/diagnostics11091719 
  • “In conclusion, there is little doubt that AI technology will benefit almost all medical personnel, ranging from specialty physicians to paramedics, in the future. Furthermore, patients should benefit from AI technology directly via mobile applications. Physicians should collaborate with the different stakeholders within the AI ecosystem to provide ethical, practical, user-friendly, and cost-effective solutions that reduce the gap between research settings and applications in clinical practice. Collaborations with regulators, patient advocates, AI companies, technology giants, and venture capitalists will help move the field forward.”
    A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology
    Akihiko Oka  , Norihisa Ishimura and Shunji Ishihara  
    Diagnostics 2021, 11, 1719. https://doi.org/10.3390/diagnostics11091719 
  • ”Early detection of postoperative complications, including organ failure, is pivotal in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an era of increasing health-care costs and limited financial resources, identifying surgical patients at a high risk of postoperative complications and providing personalised precision medicine-based treatment strategies provides an obvious pathway for reducing patient morbidity and mortality. We aimed to leverage deep learning to create, through training on structured electronic health-care data, a multilabel deep neural network to predict surgical postoperative complications that would outperform available models in surgical risk prediction.”
    Assessing the utility of deep neural networks in predicting  postoperative surgical complications: a retrospective study  
    Alexander Bonde  et al.
  • "In this retrospective study, we used data on 58 input features, including demographics, laboratory values, and 30-day postoperative complications, from the American College of Surgeons (ACS) National Surgical Quality Improvement Program database, which collects data from 722 hospitals from around 15 countries. We queried the entire adult (≥18 years) database for patients who had surgery between Jan 1, 2012, and Dec 31, 2018. We then identified all patients who were treated at a large midwestern US academic medical centre, excluded them from the base dataset, and reserved this independent group for final model testing. We then randomly created a training set and a validation set from the remaining cases.”
    Assessing the utility of deep neural networks in predicting  postoperative surgical complications: a retrospective study  
    Alexander Bonde  et al.
    Lancet Digit Health 2021; 3: e471–85 Lancet Digit Health 2021; 3: e471–85 
  • “We have developed unified prediction models, based on deep neural networks, for predicting surgical postoperative complications. The models were generally superior to previously published surgical risk prediction tools and appeared robust to changes in the underlying patient population. Deep learning could offer superior approaches to surgical risk prediction in clinical practice.”
    Assessing the utility of deep neural networks in predicting  postoperative surgical complications: a retrospective study  
    Alexander Bonde  et al.
    Lancet Digit Health 2021; 3: e471–85 
  • Implications of all the available evidence  
    ”Our deep learning models were superior to previously published surgical risk prediction tools, despite the increasingly rigorous standards for model validation. Our algorithms might be used by clinicians to help guide future preoperative, intraoperative, and postoperative risk management, serving as an important step towards personalised medicine in surgery. A clinical trial is required to identify whether the use of deep learning models can help to reduce the incidence of surgical postoperative complications.”
    Assessing the utility of deep neural networks in predicting  postoperative surgical complications: a retrospective study  
    Alexander Bonde  et al.
    Lancet Digit Health 2021; 3: e471–85 
  • Background: or Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of mortality in the world with the overall 5-year survival rate of 6%. The survival of patients with PDAC is closely related to recurrence and therefore it is necessary to identify the risk factors for recurrence. This study uses artificial intelligence approaches and multi-center registry data to analyze the recurrence of pancreatic cancer after surgery and its major determinants.  
    Results: Based on variable importance from the random forest, major predictors of disease-free survival after surgery were tumor size (0.00310), tumor grade (0.00211), TNM stage (0.00211), T stage (0.00146) and lymphovascular invasion (0.00125). The coefficients of these variables were statistically significant in the Cox model (p < 0.05). The C-Index averages of the random forest and the Cox model were 0.6805 and 0.7738, respectively.  
    Conclusions: This is the first artificial-intelligence study with multi-center registry data to predict disease-free survival after the surgery of pancreatic cancer. The findings of this methodological study demonstrate that artificial intelligence can provide a valuable decision-support system for treating patients undergoing surgery for pancreatic cancer. However, at present, further studies are needed to demonstrate the actual benefit of applying machine learning algorithms in clinical practice.  
    Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study  
    Kwang-Sig Lee et al.
    International Journal of Surgery 93 (2021) 106050 
  • Results: Based on variable importance from the random forest, major predictors of disease-free survival after surgery were tumor size (0.00310), tumor grade (0.00211), TNM stage (0.00211), T stage (0.00146) and lymphovascular invasion (0.00125). The coefficients of these variables were statistically significant in the Cox model (p < 0.05). The C-Index averages of the random forest and the Cox model were 0.6805 and 0.7738, respectively.  
    Conclusions: This is the first artificial-intelligence study with multi-center registry data to predict disease-free survival after the surgery of pancreatic cancer. The findings of this methodological study demonstrate that artificial intelligence can provide a valuable decision-support system for treating patients undergoing surgery for pancreatic cancer. However, at present, further studies are needed to demonstrate the actual benefit of applying machine learning algorithms in clinical practice.  
    Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study  
    Kwang-Sig Lee et al.
    International Journal of Surgery 93 (2021) 106050 
  • Background: Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.
    Methods: A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN.
    Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
    Toshiaki Hirasawa et al.
    Gastric Cancer (2018) 21:653–660
  • Results: The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface.
    Conclusion: The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.
    Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
    Toshiaki Hirasawa et al.
    Gastric Cancer (2018) 21:653–660
  • “In conclusion, we developed a CNN system for detecting gastric cancer using stored endoscopic images, which processed extensive independent images in a very short time. The clinically relevant diagnostic ability of the CNN offers a promising applicability to daily clinical practice for reducing the burden of endoscopists as well as telemedicine in remote and rural areas as well as in developing countries where the number of endoscopists is limited.”
    Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
    Toshiaki Hirasawa et al.
    Gastric Cancer (2018) 21:653–660
  • AI and Medicine: Changing the Workflow
    - GI Endoscopy
    - Acute findings in CT (pneumothorax, intracranial bleed, PE)
    - Goal is to increase physician accuracy and perhaps increase workflow/through-put
  • “Artificial intelligence (AI) is coming to medicine in a big wave. From making diagnosis in various medical conditions, following the latest advancements in scientific literature, suggesting appropriate therapies, to predicting prognosis and outcome of diseases and conditions, AI is offering unprecedented possibilities to improve care for patients. Gastroenterology is a field that AI can make a significant impact. This is partly because the diagnosis of gastrointestinal conditions relies a lot on image-based investigations and procedures (endoscopy and radiology). AI-assisted image analysis can make accurate assessment and provide more information than conventional analysis. AI integration of genomic, epigenetic, and metagenomic data may offer new classifications of gastrointestinal cancers and suggest optimal personalized treatments.”
    Artificial intelligence in gastroenterology: where are we heading?
    Joseph JY Sung, Nicholas CH Poon
    Front. Med. https://doi.org/10.1007/s11684-020-0742-4
  • “AI integration of genomic, epigenetic, and metagenomic data may offer new classifications of gastrointestinal cancers and suggest optimal personalized treatments. In managing relapsing and remitting diseases such as inflammatory bowel disease, irritable bowel syndrome, and peptic ulcer bleeding, convoluted neural network may formulate models to predict disease outcome, enhancing treatment efficacy. AI and surgical robots can also assist surgeons in conducting gastrointestinal operations. While the advancement and new opportunities are exciting, the responsibility and liability issues of AI-assisted diagnosis and management need much deliberations.”
    Artificial intelligence in gastroenterology: where are we heading?
    Joseph JY Sung, Nicholas CH Poon
    Front. Med. https://doi.org/10.1007/s11684-020-0742-4
  • “AI has another major potential in healthcare: to predict the clinical outcome of patients on the basis of clinical data set, genomic information, and medical images. Cardiologists have developed algorithms to assess the risk of cardiovascular disease and claimed that their prediction is superior to existing scoring systems. By analyzing echocardiograms, deep CNN model claimed to predict the mortality of patients with heart failure. Risk assessment and prediction of outcome have always been a challenge in public health and clinical medicine. Now, AI is offering a new direction to these challenges.
    Artificial intelligence in gastroenterology: where are we heading?
    Joseph JY Sung, Nicholas CH Poon
    Front. Med. https://doi.org/10.1007/s11684-020-0742-4
  • “However, when AI-assisted endoscopy and surgery are put into daily use, who should take the responsibility of clinical decisions? When a malignant polyp is missed or misdiagnosed as benign hence left unresected, when the depth of invasion is assessed to be superficial and surgery is not offered, and when the resection margin of endoscopic dissection is wrongly assessed and follow-up operations are not performed, these scenarios might lead to disastrous outcomes or even medical–legal consequences. Where should medical liability rest on?”
    Artificial intelligence in gastroenterology: where are we heading?
    Joseph JY Sung, Nicholas CH Poon
    Front. Med. https://doi.org/10.1007/s11684-020-0742-4
  • Objective: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods.
    Results: The MLA achieved an AUROC of 0.88, 0.84, and 0.83 for sepsis onset and 24 and 48 h prior to onset, respectively. These values were superior to those of SIRS (0.66), MEWS (0.61), SOFA (0.72), and qSOFA (0.60) at time of onset. When trained on UCSF data and tested on BIDMC data, sepsis onset AUROC was 0.89.
    Discussion and conclusion: The MLA predicts sepsis up to 48 h in advance and identifies sepsis onset more accurately than commonly used tools, maintaining high performance for sepsis detection when trained and tested on separate datasets.
    Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
    Barton C et al.
    Computers in Biology and Medicine 109 (2019) 79-84
  • Objective: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods.
    Discussion and conclusion: The MLA predicts sepsis up to 48 h in advance and identifies sepsis onset more accurately than commonly used tools, maintaining high performance for sepsis detection when trained and tested on separate datasets.
    Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
    Barton C et al.
    Computers in Biology and Medicine 109 (2019) 79-84
  • “The machine learning algorithm assessed in this study is capable of predicting sepsis up to 48 h in advance of onset with an AUROC of 0.83. This performance exceeds that of commonly used detection methods at time of onset, and may in turn lead to improved patient outcomes through early detection and clinical intervention.”
    Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
    Barton C et al.
    Computers in Biology and Medicine 109 (2019) 79-84

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