Classification of Body Regions Based on MRI Log Files

Kuhnert, Nadine; Lindenmayr, Oliver; Maier, Andreas

doi:10.1007/978-3-319-59162-9_11

Cited by 12 publications

(8 citation statements)

References 4 publications

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“…Log files have previously been used elsewhere to assign protocols to body regions [6] and to identify prolonged examinations and to monitor optimization approaches in which, for example, protocols were shortened [7,8]. No further analyses of these data were carried out, which may be due to their non-automated generation.…”

Section: Discussionmentioning

confidence: 99%

Automatic, log file-based process analysis of a clinical 1.5T MR scanner: a proof-of-concept study

Frydrychowicz

Boppel

Sieber

et al. 2021

Rofo

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Purpose In light of the steadily increasing need for economical efficacy and capacity utilization it was the aim of this proof-of-concept work to implement an automated logfile-based analysis tool for MRI scanner utilization and to establish a process analysis. As a primary step, analyses of scanner and protocol utilization, parametrization of protocol processes, their durations, age dependency, and scan efficacy were to be tested. Materials and Methods Logfiles were continuously extracted from a 1.5 T MR scanner (Philips Achieva) and automatically explored for relevant scan parameters. Parameters were extracted into a database and logically combined to protocol parameters. Visualization was achieved using PowerBI (Microsoft, USA). Data aggregation comprised a day-based and protocol-based strategy. In addition, age- and regional-based testing was performed. The frequency of protocol usage was evaluated and those protocols with frequent usage compared regarding efficacy to those rarely used. Results After successful technical implementation, 3659 MR exams were available for further analysis. Out of a plethora of parameters, those relevant to the understanding of the scan process were identified. The initial results mirror the daily scanner usage and allow identifying, e. g., shortened scanner usage on Fridays or longer examination times in children. A scan efficacy of 69.6 ± 17.6 % excluding preparation process was identified as a parameter with high potential to be optimized in daily routine. Conclusion The logfile-based analysis of MR scanner processes was successfully introduced and holds the promise to be extended into a comprehensive analytic tool for the analysis and optimization of scanner processes. In combination with other variables from the departmental or institutional infrastructure or patient-specific information such tool may be developed into a intelligent steering tool. Key Points: Citation Format

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Section: Discussionmentioning

confidence: 99%

Automatic, log file-based process analysis of a clinical 1.5T MR scanner: a proof-of-concept study

Frydrychowicz

Boppel

Sieber

et al. 2021

Rofo

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“…The need for automatic parsers which are adaptive to gradual changes has been described, 7 however, not yet addressed, holistically. For example, in Kuhnert et al, 34 the problem of incomplete parsing results was treated as a post-processing task. A classification method was employed in order to complete missing information in log files from old versioned software.…”

Section: Related Workmentioning

confidence: 99%

FlexParser—The adaptive log file parser for continuous results in a changing world

Rücker

Maier

2022

J Software Evolu Process

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Any modern system writes events into files, called log files. Those contain crucial information which are subject to various analyses. Examples range from cybersecurity, intrusion detection over usage analyses to trouble shooting. Before data analysis is possible, desired information needs to be extracted first out of the semi-structured log messages. State-of-the-art event parsing often assumes static log events. However, any modern system is updated consistently and with updates also log file structures can change. We call those changes "mutation" and study parsing performance for different mutation cases. Latest research discovers mutations using anomaly detection post mortem, however, does not cover actual continuous parsing. Thus, we propose a novel and flexible parser, called FlexParser, which can extract desired values despite gradual changes in the log messages. It implies basic text preprocessing followed by a supervised Deep Learning method. We train a stateful LSTM on parsing one event per data set. Statefulness enforces the model to learn log message structures across several examples. Our model was tested on seven different, publicly available log file data sets and various kinds of mutations. Exhibiting an average F1-Score of 0.98, it outperforms other Deep Learning methods as well as state-of-the-art unsupervised parsers.

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“…Event logs from imaging modalities were used in [12] to retrieve the examined body region in MRI exams from sequence parameters with a classification accuracy of 94.7% and in [13] to classify interventional X-ray exams into respective procedures or examined anatomy, reaching a classification accuracy of 92.7%.…”

Section: Related Workmentioning

confidence: 99%

“…To summarize, it can be stated that a large part of the research has focused on the prediction of diagnosis codes [5–10] rather than on procedure billing codes [11]. Additionally, the data basis of the published methods for predicting medical codes usually consisted of discharge summaries and free-text reports, although there were recent attempts to utilize MRI log data for data analytic purposes [12, 13]. Thus, this paper is one of few works on automation of procedure coding, and to the best of our knowledge, it is the first attempt to focus on the prediction of procedure billing codes from data excluding EMRs or EHRs.…”

Section: Related Workmentioning

confidence: 99%

Automated Billing Code Retrieval from MRI Scanner Log Data

et al. 2019

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Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors.

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Classification of Body Regions Based on MRI Log Files

Cited by 12 publications

References 4 publications

Automatic, log file-based process analysis of a clinical 1.5T MR scanner: a proof-of-concept study

Automatic, log file-based process analysis of a clinical 1.5T MR scanner: a proof-of-concept study

FlexParser—The adaptive log file parser for continuous results in a changing world

Automated Billing Code Retrieval from MRI Scanner Log Data

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