key: cord-0915393-443i1haz authors: Egli, Adrian title: Digitalization, clinical microbiology and infectious diseases date: 2020-07-02 journal: Clin Microbiol Infect DOI: 10.1016/j.cmi.2020.06.031 sha: 6acc2f30c73976baa58f0eb4f884966689c6a0fd doc_id: 915393 cord_uid: 443i1haz nan In recent years, digitalization and artificial intelligence made tremendous progress. In medicine, datadriven technologies are especially applicable in areas with a high degree of automation and standardization of data 1, 2 . Substantial advances have as well been reported in clinical microbiology, but translation into routine application remains a long process with several technical and regulatory hurdles. Some of the low hanging fruits for diagnostics scenarios include (i) dashboards to interconnect and visualize microbiology data 3, 4 , (ii) automated analysis of images such as microscopy slides 5 or agar plates 6, 7 , and (iii) association of genome sequences and proteomic profiles with pathogen phenotypes 8, 9 . 14 . Due to issues in data handling, two prominent published COVID-19 articles were recently retracted 15, 16 . Journals clearly need standards for data and code sharing. The FAIR principles provide an excellent guidance 17 . Although, software code and tools are often shared on GitHub (github.com) 18 , the details provided are often limited with missing explanatory code books or instructions. Proper data and code handling policies should be part of the new research quality standard and will allow independent validation of machine learning algorithms and dataset. Smith and Kirby report on applications in modern image analysis 19 20, 21 . Similarly, based on pattern recognition, single bacterial colonies growing on agar plates can be categorized or even identified 6, 7 . Both applications, automated microscopy and agar plate inspection, will likely radically change the workflow in modern diagnostic laboratories 22 . Perhaps parallel to how we have embraced MALDI-TOF mass-spectrometry for identification, making biochemical tests almost superfluous 23 . However, there may be potential for extracting additional information from MALDI-TOF spectra. Weis and colleagues look into this key technology 24 and summarize algorithms to link spectral profiles to microbiological phenotypes. In their review, 36 studies using machine learning for species identification and antibiotic susceptibility testing were identified. Most commonly used machine learning techniques included support vector machines, genetic algorithms, artificial neural networks, and quick classifiers. Within the studies identified, a wide range of qualities were noted and only four studies validated their findings 24 . All authors highlight the need for validated algorithms. Validation is also a key point in the regulatory process and impacts reimbursement. In May 2021, the medical device and in-vitro medical device regulations of Europe will steer software with a diagnostic, monitoring or therapeutic purpose (http://ec.europa.eu/growth/sectors/medical-devices/regulatory-framework/), forming the basis for CE labelling including machine learning based algorithms in clinical microbiology. Both academia and industry will benefit from standards in data and code handling as this process will support validation and further build trust in computational models and methods 25, 26 . A process additionally fuelled by (i) well-designed clinical studies and (ii) cross-validation to known and well-established statistical approaches. Ethical and legal aspects should also be raised if such algorithms are to be integrated in personalized and public health medicine 27 . As illustrated, during the COVID-19 crisis, multiple models have predicted different outcomes 28,29 of e.g. fatality rates and impact of the lockdown. In public health emergencies high quality real-time data has to be available in machine readable formats for the scientific community. Such infrastructure for public health monitoring needs to be further developed. If public health decisions rely on such models, in return models should to be validated similar to algorithms in personalized medicine as the impact for the general population and economics is significant. Clearly, an interesting and challenging time for clinical microbiology and infectious disease is ahead. Standards in data and code handling is a first step, which will allow us to use the opportunities of digitalization and machine learning to improve diagnostics and patient care. Improving data workflow systems with cloud services and use of open data for bioinformatics research Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies Retraction: Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19 RETRACTED: Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis A FAIR guide for data providers to maximise sharing of human genomic data Using Genomics to Track Global Antimicrobial Resistance. Front Public Health 7 Image analysis and artificial intelligence in infectious disease diagnostics Collaborative intelligence and gamification for on-line malaria species differentiation Pocket laboratories Implementation of the WASPLab and first year achievements within a university hospital Matrix-assisted laser desorption ionization time-of-flight mass spectrometry in clinical microbiology: An updating review Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review Bridging the "last mile" gap between AI implementation and operation: "data awareness" that matters High-performance medicine: the convergence of human and artificial intelligence Clinical applications of machine learning algorithms: beyond the black box Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe