Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network

Wilder-Smith, Adrian; Yang, Shan; Weikert, Thomas; Bremerich, Jens; Haaf, Philip; Segeroth, Martin; Ebert, Lars C.; Sauter, Alexander; Sexauer, Raphael

doi:10.3390/diagnostics12051045

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…In future research, we would like to use the timestamp analyses prospectively for workflow changes. Specifically, we want to introduce different deep learning applications for cardiothoracic imaging [ 16 , 17 ] as well as multimedia enhanced reports (with hyperlinks to the image findings) and investigate their influence on the current reporting time.…”

Section: Discussionmentioning

confidence: 99%

Time Is Money: Considerations for Measuring the Radiological Reading Time

Sexauer

Bestler

2022

J. Imaging

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Timestamps in the Radiology Information System (RIS) are a readily available and valuable source of information with increasing significance, among others, due to the current focus on the clinical impact of artificial intelligence applications. We aimed to evaluate timestamp-based radiological dictation time, introduce timestamp modeling techniques, and compare those with prospective measured reporting. Dictation time was calculated from RIS timestamps between 05/2010 and 01/2021 at our institution (n = 108,310). We minimized contextual outliers by simulating the raw data by iteration (1000, vector size (µ/sd/λ) = 100/loop), assuming normally distributed reporting times. In addition, 329 reporting times were prospectively measured by two radiologists (1 and 4 years of experience). Altogether, 106,127 of 108,310 exams were included after simulation, with a mean dictation time of 16.62 min. Mean dictation time was 16.05 min head CT (44,743/45,596), 15.84 min for chest CT (32,797/33,381), 17.92 min for abdominal CT (n = 22,805/23,483), 10.96 min for CT foot (n = 937/958), 9.14 min for lumbar spine (881/892), 8.83 min for shoulder (409/436), 8.83 min for CT wrist (1201/1322), and 39.20 min for a polytrauma patient (2127/2242), without a significant difference to the prospective reporting times. In conclusion, timestamp analysis is useful to measure current reporting practice, whereas body-region and radiological experience are confounders. This could aid in cost–benefit assessments of workflow changes (e.g., AI implementation).

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Section: Discussionmentioning

confidence: 99%

Time Is Money: Considerations for Measuring the Radiological Reading Time

Sexauer

Bestler

2022

J. Imaging

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“…AI has proven its usefulness in pericardial diseases; from the diagnosis of liquid pericarditis based on ECG [ 193 ] to the measurement of pericardial fluid based on echocardiography [ 194 ], automatic detection and classification of pericarditis using CT images of the chest [ 195 ], and prediction of fluid pericarditis in patients undergoing cardiac stimulation [ 196 ] or in breast cancer patients [ 192 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

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