The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics

In this study, we propose a framework to help the MPE take up a unique and important role at the introduction of AI solutions in clinical practice, and more in particular at procurement, acceptance, commissioning and QA. Material and methods: The steps for the introduction of Medical Radiological Equipment in a hospital setting were extrapolated to AI tools. Literature review and in-house experience was added to prepare similar, yet dedicated test methods. Results: Procurement starts from the clinical cases to be solved and is usually a complex process with many stakeholders and possibly many candidate AI solutions. Specific KPIs and metrics need to be defined. Acceptance testing follows, to verify the installation and test for critical exams. Commissioning should test the suitability of the AI tool for the intended use in the local institution. Results may be predicted from peer reviewed papers that treat representative populations. If not available, local data sets can be prepared to assess the KPIs, or 'virtual clinical trials' could be used to create large, simulated test data sets. Quality assurance must be performed periodically to verify if KPIs are stable, especially if the software is upscaled or upgraded, and as soon as selflearning AI tools would enter the medical practice. Discussion: MPEs are well placed to bridge between manufacturer and medical team and help from procurement up to reporting to the management board. More work is needed to establish consolidated test protocols.

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“…A whole new curriculum is being established [52]. The interested reader can refer to a recently published article on the subject [53].…”

Section: Discussionmentioning

confidence: 99%

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Bosmans

Zanca²,

Gelaude

2021

Physica Medica

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“…Another essential aspect that needs to be satisfied is the compliance to the currently adopted regulations 199 , where vendors can offer vital support 200,201 .…”

Section: Deep Learning Considerations and Trendsmentioning

confidence: 99%

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

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Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of positron emission tomography (PET).Appropriate database searching was performed on journal articles published between January 2014 and December 2020.The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DLbased sCT generation was evaluated, assessing the clinical readiness of the presented methods.

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“…Despite the encouraging results that we obtained, the ML-based decision system we have developed is not yet ready to be applied in clinical workflows. Before application-specific ML algorithms can be successfully translated into clinics, a number of challenges must indeed be overcome, including the harmonization of different data samples [7,59], the reliability and reproducibility of the results [7,10,60] the interpretability of the models and the results [59,61], and the compliance with current regulations [62].…”

Section: Tablementioning

confidence: 99%

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

Ubaldi

Valenti²,

Borgese

et al. 2021

Physica Medica

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Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC).We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra-and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets.We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine).According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.

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The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics

Cited by 55 publications

References 26 publications

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

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