Machine Learning and Deep Neural Network Applications in the Thorax

Rémy‐Jardin, Martine; Faivre, J; Kaergel, Rainer; Hutt, Antoine; Felloni, Paul; Khung, Suonita; Lejeune, Anne-Laure; Giordano, Jessica; Rémy, Jacques

doi:10.1097/rti.0000000000000492

Cited by 19 publications

(4 citation statements)

References 41 publications

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“…Clinical relevance is often presented in 3 main categories: improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients for the clinician. Accuracy is typically reported for classification and diagnostic tasks where there are gold-standard diagnostic criteria that the model can compare with, such as the detection of tumors, pulmonary embolism, or sepsis 38-40 . An important consideration when contextualizing the accuracy of an algorithm is the number of classifications it is making.…”

Section: Methodsmentioning

confidence: 99%

Understanding Artificial Intelligence and Predictive Analytics

et al. 2022

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Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics.Machine learning will soon become critical in the day-to-day operations of orthopaedic practice; therefore, it is imperative that providers become accustomed to and familiar with not only the terminology but also the fundamental techniques behind the technology.A foundation of knowledge regarding machine learning is critical for physicians so they can begin to understand the details in the algorithms that are being developed, which provide improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients.

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Section: Methodsmentioning

confidence: 99%

Understanding Artificial Intelligence and Predictive Analytics

et al. 2022

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“…AI could also assist radiologists in several ways. Many studies already implemented DL algorithms to enhance the radiologists’ workflow by automated triaging and flagging PE on CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients [ 23 – 25 ].…”

Section: Ai Applications In Oncologic Thoracic Emergenciesmentioning

confidence: 99%

“…AI could also assist radiologists in several ways. Many studies already implemented DL algorithms to enhance the radiologists' workflow by automated triaging and flagging PE on CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients [23][24][25]. Indeed, early diagnosis and communication to the referring physician may lead to earlier treatment, and to achieve these results and to improve the communication between the radiologist and the clinician electronic notification systems were developed.…”

Section: Ai Applications In Oncologic Thoracic Emergenciesmentioning

confidence: 99%

Role of artificial intelligence in oncologic emergencies: a narrative review

Fanni

Greco

Rossi

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathology in metabolic, hematologic, and structural conditions. In the latter, radiologists have a pivotal role, through an accurate diagnosis useful to provide optimal patient care. Structural conditions may involve the central nervous system, thorax, or abdomen, and emergency radiologists have to know the characteristics imaging findings of each one of them. The number of oncologic emergencies is growing due to the increased incidence of malignancies in the general population and also to the improved survival of these patients thanks to the advances in cancer treatment. Artificial intelligence (AI) could be a solution to assist emergency radiologists with this rapidly increasing workload. To our knowledge, AI applications in the setting of the oncologic emergency are mostly underexplored, probably due to the relatively low number of oncologic emergencies and the difficulty in training algorithms. However, cancer emergencies are defined by the cause and not by a specific pattern of radiological symptoms and signs. Therefore, it can be expected that AI algorithms developed for the detection of these emergencies in the non-oncological field can be transferred to the clinical setting of oncologic emergency. In this review, a craniocaudal approach was followed and central nervous system, thoracic, and abdominal oncologic emergencies have been addressed regarding the AI applications reported in literature. Among the central nervous system emergencies, AI applications have been reported for brain herniation and spinal cord compression. In the thoracic district the addressed emergencies were pulmonary embolism, cardiac tamponade and pneumothorax. Pneumothorax was the most frequently described application for AI, to improve sensibility and to reduce the time-to-diagnosis. Finally, regarding abdominal emergencies, AI applications for abdominal hemorrhage, intestinal obstruction, intestinal perforation, and intestinal intussusception have been described.

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“…Prior to the start of the pandemic, the possibility to detect PTE from CT scans was already considered in some studies [6][7][8][9][10][11]. Cobelli et al performed a comparison between the CT with and without contrast medium.…”

Section: Introductionmentioning

confidence: 99%

Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

et al. 2021

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The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature.

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Machine Learning and Deep Neural Network Applications in the Thorax

Cited by 19 publications

References 41 publications

Understanding Artificial Intelligence and Predictive Analytics

Understanding Artificial Intelligence and Predictive Analytics

Role of artificial intelligence in oncologic emergencies: a narrative review

Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

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