Time-series forecasting through modelling sequences of temporally dependent observations has many industrial and scientific applications. While machine learning models have been widely used to... Show moreTime-series forecasting through modelling sequences of temporally dependent observations has many industrial and scientific applications. While machine learning models have been widely used to create time-series forecasting models, creating efficient and performant time-series forecasting models is a complex task for domain users. Automated Machine Learning (AutoML) is a growing field that aims to make the process of creating machine-learning models accessible for non-machine learning experts. This is achieved by optimising machine learning pipelines automatically. Time-series machine-learning pipelines include various specialised pre-processing steps that are not currently supported by existing AutoML systems. This dissertation investigates how AutoML can be extended to time-series data analysis problems such as time-series forecasting. Several challenges arise when developing specialised AutoML systems for time-series forecasting. For instance, advanced machine-learning pipelines that can extract time-series features and select well-suited machine-learning models need to be developed. Also, extra hyperparameters such as the window size, which shows how many historical data points are helpful, need to be optimised by the AutoML system. This dissertation addresses these issues. We provide a comprehensive overview of the AutoML research field, including hyperparameter optimisation techniques, neural architecture search, and existing AutoML systems. Next, we investigate the use of AutoML for short-term forecasting, single-step ahead time-series forecasting, and multi-step time-series forecasting with time-series features. Show less
PURPOSE: Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single “optimal” plan, finding multiple, yet... Show morePURPOSE: Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single “optimal” plan, finding multiple, yet different near-optimal plans, can be an insightful approach to support radiation oncologists in finding the plan they are looking for. METHODS AND MATERIALS: BRIGHT is a flexible AI-based optimization method for brachytherapy treatment planning that has already been shown capable of finding high-quality plans that trade-off target volume coverage and healthy tissue sparing. We leverage the flexibility of BRIGHT to find plans with similar dose-volume criteria, yet different dose distributions. We further describe extensions that facilitate fast plan adaptation should planning aims need to be adjusted, and straightforwardly allow incorporating hospital-specific aims besides standard protocols. RESULTS: Results are obtained for prostate (n=12) and cervix brachytherapy (n=36). We demonstrate the possible differences in dose distribution for optimized plans with equal dosevolume criteria. We furthermore demonstrate that adding hospital-specific aims enables adhering to hospital-specific practice while still being able to automatically create cervix plans that more often satisfy the EMBRACE-II protocol than clinical practice. Finally, we illustrate the feasibility of fast plan adaptation. CONCLUSIONS: Methods such as BRIGHT enable new ways to construct high-quality treatment plans for brachytherapy while offering new insights by making explicit the options one has. In particular, it becomes possible to present to radiation oncologists a manageable set of alternative plans that, from an optimization perspective are equally good, yet differ in terms of coverage-sparing trade-offs and shape of the dose distribution Show less
AI-powered emotion recognition, typing with thoughts or eavesdropping virtual assistants: three non-fictional examples illustrate how AI may impact society. AI-related products and services... Show moreAI-powered emotion recognition, typing with thoughts or eavesdropping virtual assistants: three non-fictional examples illustrate how AI may impact society. AI-related products and services increasingly find their way into daily life. Are the EU's fundamental rights to privacy and data protection equipped to protect individuals effectively? In addressing this question, the dissertation concludes that no new legal framework is needed. Instead, adjustments are required. First, the extent of adjustments depends on the AI discipline. There is nothing like 'the AI'. AI covers various concepts, including the disciplines machine learning, natural language processing, computer vision, affective computing and automated reasoning. Second, the extent of adjustments depends on the type of legal problem: legal provisions are violated (type 1), cannot be enforced (type 2) or are not fit for purpose (type 3). Type 2 and 3 problems require either adjustments of current provisions or new judicial interpretations. Two instruments might be helpful for more effective legislation: rebuttable presumptions and reversal of proof. In some cases, the solution is technical, not legal. Research in AI should solve reasoning deficiencies in AI systems and their lack of common sense. Show less
Background: Because anti-neutrophil cytoplasmatic antibody (ANCA)-associated vasculitis (AAV) is a rare, lifethreatening, auto-immune disease, conducting research is difficult but essential. A long... Show moreBackground: Because anti-neutrophil cytoplasmatic antibody (ANCA)-associated vasculitis (AAV) is a rare, lifethreatening, auto-immune disease, conducting research is difficult but essential. A long-lasting challenge is to identify rare AAV patients within the electronic-health-record (EHR)-system to facilitate real-world research. Artificial intelligence (AI)-search tools using natural language processing (NLP) for text-mining are increasingly postulated as a solution.Methods: We employed an AI-tool that combined text-mining with NLP-based exclusion, to accurately identify rare AAV patients within large EHR-systems (>2.000.000 records). We developed an identification method in an academic center with an established AAV-training set (n = 203) and validated the method in a non-academic center with an AAV-validation set (n = 84). To assess accuracy anonymized patient records were manually reviewed.Results: Based on an iterative process, a text-mining search was developed on disease description, laboratory measurements, medication and specialisms. In the training center, 608 patients were identified with a sensitivity of 97.0 % (95%CI [93.7, 98.9]) and positive predictive value (PPV) of 56.9 % (95%CI [52.9, 60.1]). NLP-based exclusion resulted in 444 patients increasing PPV to 77.9 % (95%CI [73.7, 81.7]) while sensitivity remained 96.3 % (95%CI [93.8, 98.0]). In the validation center, text-mining identified 333 patients (sensitivity 97.6 % (95%CI [91.6, 99.7]), PPV 58.2 % (95%CI [52.8, 63.6])) and NLP-based exclusion resulted in 223 patients, increasing PPV to 86.1 % (95%CI [80.9, 90.4]) with 98.0 % (95%CI [94.9, 99.4]) sensitivity. Our identification method outperformed ICD-10-coding predominantly in identifying MPO+ and organ-limited AAV patients.Conclusions: Our study highlights the advantages of implementing AI, notably NLP, to accurately identify rare AAV patients within large EHR-systems and demonstrates the applicability and transportability. Therefore, this method can reduce efforts to identify AAV patients and accelerate real-world research, while avoiding bias by ICD-10-coding. Show less
The recent surge in deployment and use of generative machine learning models has sparked an interest in the relationships between AI and creativity, or more specifically into the question and... Show moreThe recent surge in deployment and use of generative machine learning models has sparked an interest in the relationships between AI and creativity, or more specifically into the question and debate of whether machines can exhibit human-level creativity. This is by no means a new discussion, going back in time decades if not centuries. The debate was approached from multiple angles, and a general consensus was not yet reached. In this position paper, we present the long-standing debate as it formed across various fields such as cognitive science, philosophy, and computing, approaching it mainly from a historical perspective. Along the way we identify how the various views relate to recent developments in machine learning models and argue our own position regarding the question of whether machines can exhibit human-level creativity. As such we aim to involve computer scientists and AI practitioners into the ongoing debate. Show less
Hubers, D.; Potters, W.; Paalvast, O.; Jonge, S. de; Doelkahar, B.; Tannemaat, M.; ... ; Verhamme, C. 2023
ObjectiveTo develop an artificial neural network (ANN) for classification of motor unit action potential (MUAP) duration in real-word, unselected and uncleaned needle electromyography (n-EMG)... Show moreObjectiveTo develop an artificial neural network (ANN) for classification of motor unit action potential (MUAP) duration in real-word, unselected and uncleaned needle electromyography (n-EMG) recordings.MethodsTwo nested ANN models were trained, the first discerning muscle rest, contraction and artifacts in n-EMG recordings from 2674 individual muscles from 326 patients obtained as part of daily care. The second ANN model subsequently used segments labeled as contraction for prediction of prolonged, normal and shortened MUAPs. Model performance was assessed in one internal and two external validation datasets of 184, 30 and 50 muscles, respectively.ResultsThe first model discerned rest, contraction and artifacts with an accuracy of 96%. The second model predicted prolonged, normal and shortened MUAPs with an accuracy of 67%, 83% and 68% in the different validation sets.ConclusionsWe developed a two-step ANN that classifies rest, muscle contraction and artifacts from real-world n-EMG recordings with very high accuracy. MUAP duration classification had moderate accuracy.SignificanceThis is the first study to show that an ANN can classify MUAPs in real-world n-EMG recordings highlighting the potential for AI assisted MUAP classification as a clinical tool. Show less
This thesis looks at Artificial Intelligence (AI) and its potential to revolutionise the healthcare sector. The first part of this thesis focuses on the responsible development and validation of AI... Show moreThis thesis looks at Artificial Intelligence (AI) and its potential to revolutionise the healthcare sector. The first part of this thesis focuses on the responsible development and validation of AI-based clinical prediction algorithms, exploring the prime considerations in this process. The second part of this thesis addresses the opportunities for classical statistics and machine learning techniques for developing prediction algorithms. It also examines the performance, potential, and challenges of AI prediction algorithms for clinical practice. The conclusion states that cross-discipline collaboration, exchangeability of knowledge and results, and validation of AI for healthcare practice are essential for realising the potential of AI in healthcare. Show less
Filippo, O. de; Cammann, V.L.; Pancotti, C.; Vece, D. di; Silverio, A.; Schweiger, V.; ... ; Templin, C. 2023
AimsTakotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform... Show moreAimsTakotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles.Methods and resultsA ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85–0.92), a sensitivity of 0.85 (0.78–0.95) and a specificity of 0.76 (0.74–0.79) in the internal validation cohort and an AUC of 0.82 (0.73–0.91), a sensitivity of 0.74 (0.61–0.87) and a specificity of 0.79 (0.77–0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort.ConclusionA ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death. Show less
Stein. N. van; Winter, R. de; Bäck, T.H.W.; Kononova, A. V. 2023
Background Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the... Show moreBackground Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential impact. Methods We conducted a pilot feasibility study from November 22nd, 2021 to June 9th, 2022 with a mixed-methods design on implementation of an AI-based mHealth app for skin cancer detection in three primary care practices in the Netherlands (Rotterdam, Leiden and Katwijk). The primary outcome was the inclusion and successful participation rate of patients and general practitioners (GPs). Secondary outcomes were the reasons, facilitators and barriers for successful participation and the potential impact in both pathways for future sample size calculations. Patients were offered use of an AI-based mHealth app before consulting their GP. GPs assessed the patients blinded and then unblinded to the app. Qualitative data included observations and audio-diaries from patients and GPs and focus-groups and interviews with GPs and GP assistants. Findings Fifty patients were included with a median age of 52 years (IQR 33.5-60.3), 64% were female, and 90% had a light skin type. The average patient inclusion rate was 4-6 per GP practice per month and 84% (n = 42) successfully participated. Similarly, in 90% (n = 45 patients) the GPs also successfully completed the study. GPs never changed their working diagnosis, but did change their treatment plan (n = 5) based on the app's assessments. Notably, 54% of patients with a benign skin lesion and low risk rating, indicated that they would be reassured and cancel their GP visit with these results (p < 0.001). Interpretation Our findings suggest that studying implementation of an AI-based mHealth app for detection of skin cancer in the hands of patients or as a diagnostic tool used by GPs in primary care appears feasible. Preliminary results indicate potential to further investigate both intended use settings. Show less
The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [F-18]FDG PET/CT lymphoma images and evaluate their influence on tumor... Show moreThe objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [F-18]FDG PET/CT lymphoma images and evaluate their influence on tumor quantification. All lymphoma lesions identified in 65 whole-body [F-18]FDG PET/CT staging images were segmented by two experienced observers using manual and semiautomatic methods. Semiautomatic segmentation using absolute and relative thresholds, k-means and Bayesian clustering, and a self-adaptive configuration (SAC) of k-means and Bayesian was applied. Three state-of-the-art deep learning-based segmentations methods using a 3D U-Net architecture were also applied. One was semiautomatic and two were fully automatic, of which one is publicly available. Dice coefficient (DC) measured segmentation overlap, considering manual segmentation the ground truth. Lymphoma lesions were characterized by 31 features. Intraclass correlation coefficient (ICC) assessed features agreement between different segmentation methods. Nine hundred twenty [F-18]FDG-avid lesions were identified. The SAC Bayesian method achieved the highest median intra-observer DC (0.87). Inter-observers' DC was higher for SAC Bayesian than manual segmentation (0.94 vs 0.84, p < 0.001). Semiautomatic deep learning-based median DC was promising (0.83 (Obs1), 0.79 (Obs2)). Threshold-based methods and publicly available 3D U-Net gave poorer results (0.56 <= DC <= 0.68). Maximum, mean, and peak standardized uptake values, metabolic tumor volume, and total lesion glycolysis showed excellent agreement (ICC >= 0.92) between manual and SAC Bayesian segmentation methods. The SAC Bayesian classifier is more reproducible and produces similar lesion features compared to manual segmentation, giving the best concordant results of all other methods. Deep learning-based segmentation can achieve overall good segmentation results but failed in few patients impacting patients' clinical evaluation. Show less
Dijkstra, H.; Oosterhoff, J.H.F.; Kuit, A. van de; Ijpma, F.F.A.; Schwab, J.H.; Poolman, R.W.; ... ; Hendrickx, L.A.M. 2023
Aims To develop prediction models using machine-learning (ML) algorithms for 90 -day and oneyear mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip... Show moreAims To develop prediction models using machine-learning (ML) algorithms for 90 -day and oneyear mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials.Methods This study included 2,388 patients from the HEALTH and FAITH trials, with 90 -day and oneyear mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration).Results The developed algorithms distinguished between patients at high and low risk for 90 -day and one -year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90 -day (c-statistic 0.80, calibration slope 0.95, calibration intercept-0.06, and Brier score 0.039) and one -year (c-statistic 0.76, calibration slope 0.86, calibration intercept-0.20, and Brier score 0.074) mortality prediction in the hold -out set.Conclusion Using high-quality data, the ML -based prediction models accurately predicted 90 -day and one -year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making. Show less
BackgroundMeasurement of peak velocities is important in the evaluation of heart failure. This study compared the performance of automated 4D flow cardiac MRI (CMR) with traditional transthoracic... Show moreBackgroundMeasurement of peak velocities is important in the evaluation of heart failure. This study compared the performance of automated 4D flow cardiac MRI (CMR) with traditional transthoracic Doppler echocardiography (TTE) for the measurement of mitral inflow peak diastolic velocities.MethodsPatients with Doppler echocardiography and 4D flow cardiac magnetic resonance data were included retrospectively. An established automated technique was used to segment the left ventricular transvalvular flow using short-axis cine stack of images. Peak mitral E-wave and peak mitral A-wave velocities were automatically derived using in-plane velocity maps of transvalvular flow. Additionally, we checked the agreement between peak mitral E-wave velocity derived by 4D flow CMR and Doppler echocardiography in patients with sinus rhythm and atrial fibrillation (AF) separately.ResultsForty-eight patients were included (median age 69 years, IQR 63 to 76; 46% female). Data were split into three groups according to heart rhythm. The median peak E-wave mitral inflow velocity by automated 4D flow CMR was comparable with Doppler echocardiography in all patients (0.90 +/- 0.43 m/s vs 0.94 +/- 0.48 m/s, P = 0.132), sinus rhythm-only group (0.88 +/- 0.35 m/s vs 0.86 +/- 0.38 m/s, P = 0.54) and in AF-only group (1.33 +/- 0.56 m/s vs 1.18 +/- 0.47 m/s, P = 0.06). Peak A-wave mitral inflow velocity results had no significant difference between Doppler TTE and automated 4D flow CMR (0.81 +/- 0.44 m/s vs 0.81 +/- 0.53 m/s, P = 0.09) in all patients and sinus rhythm-only groups. Automated 4D flow CMR showed a significant correlation with TTE for measurement of peak E-wave in all patients group (r = 0.73, P < 0.001) and peak A-wave velocities (r = 0.88, P < 0.001). Moreover, there was a significant correlation between automated 4D flow CMR and TTE for peak-E wave velocity in sinus rhythm-only patients (r = 0.68, P < 0.001) and AF-only patients (r = 0.81, P = 0.014). Excellent intra-and inter-observer variability was demonstrated for both parameters.ConclusionAutomated dynamic peak mitral inflow diastolic velocity tracing using 4D flow CMR is comparable to Doppler echocardiography and has excellent repeatability for clinical use. However, 4D flow CMR can potentially underestimate peak velocity in patients with AF. Show less
Pijpers, Peter B.M.J.; Voskuijl, Mark; Beeres, Robert J.M. 2023
Towards a data-driven military. A multi-disciplinary perspective assesses the use of data and information on modern conflict from different scientific and methodological disciplines, aiming to... Show moreTowards a data-driven military. A multi-disciplinary perspective assesses the use of data and information on modern conflict from different scientific and methodological disciplines, aiming to generate valuable contributions to the ongoing discourse on data, the military and modern warfare. Military Systems and Technology approaches the theme empirically by researching how data can enhance the utility of military materiel and subsequently accelerate the decision-making process. War Studies take a multidisciplinary approach to the evolution of warfare, while Military Management Studies take a holistic organisational and procedural approach. Based on their scientific protocols and research methods, the three domains put forward different research questions and perspectives, providing the unique character of this book. Show less
Targeted advertising is the primary revenue stream for the largest online platforms that act as the internet’s gatekeepers, such as Alphabet and Meta. The financial incentives drive targeted... Show moreTargeted advertising is the primary revenue stream for the largest online platforms that act as the internet’s gatekeepers, such as Alphabet and Meta. The financial incentives drive targeted advertising towards maximizing the efficiency of algorithmically matching advertisements with consumers, which typically requires building fine-grained profiles that rely on consumers’ personal data. In the European Union (EU), the protection of personal data is a fundamental right operationalized by the General Data Protection Regulation (GDPR), establishing the limits of targeted advertising to the extent that it relies on the processing of personal data. Nevertheless, as online interface design and fine-grained personalization allow platforms and other publishers new ways to influence consumers, targeted advertising is also associated with the potential for consumer manipulation.While the consumer protection framework in the EU is the primary field that protects consumers from manipulation, it has received little attention in academia in the context of targeted advertising whencompared with the GDPR. In 2022, the EU adopted proposals for the Digital Services Act (DSA) and the Digital Markets Act (DMA), which contain consumer protection rules that directly limit targeted advertising. These developments in consumer protection law may fundamentally transform the internet, as its gatekeepers are now faced with a new legal regime that regulates their primary source of revenue.This Article provides an overview of the myriad of legislation that comprises the EU consumer protection framework—including how it intersects with the data protection framework—and analyzes how andthe extent to which it coalesces to limit targeted advertising. Show less
As sustainability becomes an increasing priority throughout global society, academic and research institutions are assessed on their contribution to relevant research publications. This study... Show moreAs sustainability becomes an increasing priority throughout global society, academic and research institutions are assessed on their contribution to relevant research publications. This study compares four methods of identifying research publications related to United Nations Sustainable Development Goal 13—Climate Action (SDG 13). The four methods (Elsevier, STRINGS, SIRIS, and Dimensions) have each developed search strings with the help of subject matter experts, which are then enhanced through distinct methods to produce a final set of publications. Our analysis showed that the methods produced comparable quantities of publications but with little overlap between them. We visualized some difference in topic focus between the methods and drew links with the search strategies used. Differences between publications retrieved are likely to come from subjective interpretation of the goals, keyword selection, operationalizing search strategies, AI enhancements, and selection of bibliographic database. Each of the elements warrants deeper investigation to understand their role in identifying SDG-related research. Before choosing any method to assess the research contribution to SDGs, end users of SDG data should carefully consider their interpretation of the goal and determine which of the available methods produces the closest data set. Meanwhile, data providers might customize their methods for varying interpretations of the SDGs. Show less
Diagnosis of soil-transmitted helminth (STH) and schistosome infections relies largely on conventional microscopy which has limited sensitivity, requires highly trained personnel and is error-prone... Show moreDiagnosis of soil-transmitted helminth (STH) and schistosome infections relies largely on conventional microscopy which has limited sensitivity, requires highly trained personnel and is error-prone. Rapid advances in miniaturization of optical systems, sensors and proces-sors have enhanced research and development of digital and automated microscopes suitable for the detection of these diseases in resource-limited settings. While some studies have reported proof-of-principle results, others have evaluated the performance of working proto-types in field settings. The extensive commercialization of these innovative devices has, how-ever, not yet been achieved. This review provides an overview of recent publications (2010- 2022) on innovative field applicable optical devices which can be used for the diagnosis of STH and schistosome infections. Using an adapted technology readiness level (TRL) scale tak -ing into account the WHO target product profile (TPP) for these diseases, the developmental stages of the devices were ranked to determine the readiness for practical applications in field settings. From the reviewed 18 articles, 19 innovative optical devices were identified and ranked. Almost all of the devices (85%) were ranked with a TRL score below 8 indicating that, most of the devices are not ready for commercialization and field use. The potential lim-itations of these innovative devices were discussed. We believe that the outcome of this review can guide the end-to-end development of automated digital microscopes aligned with the WHO TPP for the diagnosis of STH and schistosome infections in resource-limited settings. Show less
Diagnosis of soil-transmitted helminth (STH) and schistosome infections relies largely on conventional microscopy which has limited sensitivity, requires highly trained personnel and is error-prone... Show moreDiagnosis of soil-transmitted helminth (STH) and schistosome infections relies largely on conventional microscopy which has limited sensitivity, requires highly trained personnel and is error-prone. Rapid advances in miniaturization of optical systems, sensors and proces-sors have enhanced research and development of digital and automated microscopes suitable for the detection of these diseases in resource-limited settings. While some studies have reported proof-of-principle results, others have evaluated the performance of working proto-types in field settings. The extensive commercialization of these innovative devices has, how-ever, not yet been achieved. This review provides an overview of recent publications (2010- 2022) on innovative field applicable optical devices which can be used for the diagnosis of STH and schistosome infections. Using an adapted technology readiness level (TRL) scale tak -ing into account the WHO target product profile (TPP) for these diseases, the developmental stages of the devices were ranked to determine the readiness for practical applications in field settings. From the reviewed 18 articles, 19 innovative optical devices were identified and ranked. Almost all of the devices (85%) were ranked with a TRL score below 8 indicating that, most of the devices are not ready for commercialization and field use. The potential lim-itations of these innovative devices were discussed. We believe that the outcome of this review can guide the end-to-end development of automated digital microscopes aligned with the WHO TPP for the diagnosis of STH and schistosome infections in resource-limited settings. Show less
Unterrainer, M.; Deroose, C.M.; Herrmann, K.; Moehler, M.; Blomqvist, L.; Cannella, R.; ... ; European Soc Gastrointestinal Abdominal Radiology (ESGAR) 2022
Background: Treatment monitoring in metastatic colorectal cancer (mCRC) relies on imaging to evaluate the tumour burden. Response Evaluation Criteria in Solid Tumors provide a framework on... Show moreBackground: Treatment monitoring in metastatic colorectal cancer (mCRC) relies on imaging to evaluate the tumour burden. Response Evaluation Criteria in Solid Tumors provide a framework on reporting and interpretation of imaging findings yet offer no guidance on a standardised imaging protocol tailored to patients with mCRC. Imaging protocol hetero-geneity remains a challenge for the reproducibility of conventional imaging end-points and is an obstacle for research on novel imaging end-points. Patients and methods: Acknowledging the recently highlighted potential of radiomics and arti-ficial intelligence tools as decision support for patient care in mCRC, a multidisciplinary, international and expert panel of imaging specialists was formed to find consensus on mCRC imaging protocols using the Delphi method. Results: Under the guidance of the European Organisation for Research and Treatment of Cancer (EORTC) Imaging and Gastrointestinal Tract Cancer Groups, the European Society of Oncologic Imaging (ESOI) and the European Society of Gastrointestinal and Abdominal Radiology (ESGAR), the EORTC-ESOI-ESGAR core imaging protocol was identified. Conclusion: This consensus protocol attempts to promote standardisation and to diminish variations in patient preparation, scan acquisition and scan reconstruction. We anticipate that this standardisation will increase reproducibility of radiomics and artificial intelligence studies and serve as a catalyst for future research on imaging end-points. For ongoing and future mCRC trials, we encourage principal investigators to support the dissemination of these im-aging standards across recruiting centres. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
