All experimental procedures and bioinformatic analyses in this work comply with ethical regulations and good scientific practices. An ethics approval for the pre-clinical experiments was not required as anonymized biological material, i.e., anonymized blood for the blood culture incubation, was provided by a blood donation center in Switzerland. The clinical study protocol for the PHENOTECH-1 study (NCT05613322) was approved by the Ethics Committee for Investigation with Medicinal Products (CEIm) in Madrid (ID 239/22), the Cantonal Commission for Ethics in Research on Human Beings (CER-VD) in Lausanne (ID 2022-02085), and the Ethics Committee of the Medical University of Innsbruck in Innsbruck (ID 1271/2022).
The strain collection used in this study consists of ATCC reference strains and clinical isolates either from patient blood samples at hospital sites or procured from strain collections (Supplementary Data1). In order to establish a methodology for nanomotion-based AST, we used the E. coli reference strain ATCC-25922, which is susceptible to ceftriaxone (CRO; ceftriaxone disodium salt hemi(heptahydrate) analytical standard, Merck & Cie, Schaffhausen, Switzerland), cefotaxime (CTX; cefotaxime sodium, Pharmaceutical Secondary Standard, Supelco, Merck & Cie, Schaffhausen, Switzerland), ciprofloxacin (CIP; ciprofloxacin, VETRANAL, analytical standard, Merck & Cie, Schaffhausen, Switzerland), and ceftazidime-avibactam (SigmaAldrich, Merck & Cie, Schaffhausen, Switzerland). Our reference strains for antibiotic resistance were BAA-2452 (resistant to CRO and CTX, blaNDM producer) and BAA-2469 (resistant to CIP). The K. pneumoniae reference isolates ATCC-27736 was susceptible to CRO.
To differentiate between resistant and susceptible phenotypes, clinical isolates were selected based on their MIC in accordance with the European Committee on Antimicrobial Susceptibility Testing (EUCAST) interpretation guidelines59. MIC strips and disk diffusion tests were performed on MH Agar plates (Mueller-Hinton agar VWR International GmbH, Dietikon, Switzerland). During all nanomotion experiments, bacteria in the measurement chamber were incubated with filtered (0.02 m Polyethersulfone, PES, Corning, or Millipore) LB (Millers LB Broth, Corning) half-diluted in deionized water (Molecular Biology Grade Water, Cytiva), hereafter referred to as 50% LB.
All bacterial strains were stored at 80C in 20% glycerol. Bacterial samples for nanomotion experiments were prepared by first thawing new cell aliquots and growing them at 37C on Columbia agar medium solid plates (Columbia blood Agar, 5% sheep blood, VWR International GmbH, Dietikon, Switzerland). These cells were then used to inoculate blood culture medium and subsequently grown for nanomotion experimentation.
We performed MIC gradient tests (MIC strips) to determine the minimal inhibitory concentration (MIC) for each antibiotic used in this study. Cell suspensions were prepared by selecting three to five colonies grown overnight (ON) at 37C on a Columbia agar plate and resuspending them in 0.9% NaCl solution (Sodium Chloride, 0.9%, HuberLab, PanReac Applichem) at a density of 0.5 McFarland units (corresponding to OD600nm=0.07). This suspension was then spread on MH plates using a sterile cotton swab to create a confluent culture. MIC strips (ceftriaxone 0.016256g/mL, ciprofloxacin 0.00232g/mL, cefotaxime 0.016256g/mL, ceftazidime 0.016256g/mL, and ceftazidime-avibactam 0.016/4256/4g/mL MIC test strips, Liofilchem, Roseto degli Abruzzi, Teramo, Italy) were then placed onto inoculated plates using tweezers. The plates were subsequently incubated at 37C for 1620h, with the growth inhibition area surrounding the MIC strip present after this incubation period used to interpret MICs.
While MIC strips served as the primary AST reference method, some situations presented difficult interpretations or exceeded the scale of the CRO MIC strips. Here, broth microdilution assays were performed according to EUCAST recommendations59. Furthermore, a disk diffusion assay (DDA) was performed in parallel to each sample assessed using nanomotion technology for quality assurance purposes20,60.
To facilitate bacterial attachment and prevent cellular detachment during AST recording, we incubated the cantilever with 50l of 0.1mg/ml PDL (Poly-D-Lysine hydrobromide, MP Biomedicals, Santa Ana, California, USA) diluted in molecular biology grade water (HyClone, Logan, Utah, United States) for 20min at room temperature (RT). This treatment created a homogenous positive electric charge that enabled the attachment of negatively charged bacteria. Following incubation, thePDLdrop was removed and discarded, after which the cantilever tip was gently washed with 100l of molecular biology-grade water. The sensors on the cantilever were then allowed to dry for at least 15min before use.
Spiking refers to the process of inoculating blood culture samples with artificially infected blood. Here, we cultured strains of interest on Columbia Agar plates ON at 37C, isolated a single colony, and resuspended it in 0.9% NaCl with volumes adjusted to obtain a 0.5 McFarland density. We then performed two 1:10 serial dilutions, starting with that suspension, to generate a final dilution of 1:100. Finally, 10l of the final dilution were added to 9990l of EDTA blood from a donor provided by a blood donation center in Switzerland. Blood has been received fully anonymized.
To generate spiked blood cultures, we added 10ml of artificially infected blood to either anaerobic (ANH) or aerobic (AEH) blood culture bottles (BD BACTECTM Lytic Anaerobic medium and BD BACTECTM Standard Aerobic medium Culture Vials; Becton Dickinson, Eysins, Switzerland) using a syringe. These culture bottles were then incubated until positivity, as determined by the BACTECTM 9240 automated blood culture system (Becton Dickinson), was reached. In most cases, this process took 12h or an overnight incubation.
To generate and purify bacterial pellets for nanomotion recordings, we used either the MBT Sepsityper IVD Kit (Bruker) or the direct attachment method (DA). When using the MBT Sepsityper IVD Kit, we followed the manufacturers instructions. Briefly, 1ml of blood culture was combined with 200l Lysis Buffer, mixed by vortexing, and then centrifuged for 2min at 12,000g to obtain a bacterial pellet. The supernatant was discarded, while the bacterial pellet was resuspended in 1ml of Washing Buffer. The resuspension was then centrifuged again for 1min at 12,000g to remove debris. For DA, 1ml of positive blood culture (PBC) was syringe filtered (5m pore size, Acrodisc Syringe Filters with Supor Membrane, Pall, Fribourg, Switzerland). The pellet was then used for attachment to the cantilever.
Bacterial cells from prepared pellets needed to be immobilized onto the surface of the functionalized cantilever for nanomotion recording. First, pellets were resuspended in a PBS (Phosphate Buffer Saline, Corning) solution containing 0.04% agarose. Next, the sensor was placed on a clean layer of Parafilm M (Amcor, Victoria, Australia). The tip of the sensor, containing the chip with the cantilever, was placed into contact with a single drop of bacterial cell suspension for 1min. After this, the sensor was removed, gently washed with PBS, and assessed using phase microscopy for attachment quality. In the event of unsatisfactory attachment, the sensor was re-incubated in the cell suspension for an additional 3060s, or until satisfactory attachment was achieved. We aimed for an even bacterial distribution across the sensor (Fig.1b, c, and Supplementary Fig.2). The attachment of bacteria is part of a filed patent (PCT/EP2020/087821).
Our nanomotion measurement platform, the Resistell Phenotech device (Resistell AG, Muttenz, Switzerland), comprises a stainless-steel head with a measurement fluid chamber, an active vibration damping system, acquisition and control electronics, and a computer terminal.
Nanomotion-based AST strategies utilize technologies that are well-established in atomic-force microscopy (AFM). Specifically, our nanomotion detection system is based on an AFM setup for cantilever-based optical deflection detection. However, in contrast to standard AFM devices, in the Phenotech device the light source and the photodetector are placed below the cantilever to facilitate the experimental workflow. A light beam, focused at the cantilever end, originates in a superluminescent diode (SLED) module (wavelength: 650mm, optical power: 2mW), is reflected, and reaches a four-sectional position-sensitive photodetector that is a part of a custom-made precision preamplifier (Resistell AG). The flexural deflection of the cantilever is transformed into an electrical signal, which is further processed by a custom-made dedicated electronic module (Resistell AG) and recorded using a data acquisition card (USB-6212; National Instruments, Austin, TX, USA). The device is controlled using a dedicated AST software (custom-made, Resistell AG).
The custom-made sensors used for the described experiments (Resistell AG) contain quartz-like tipless cantilevers with a gold coating acting as a mirror for the light beam (SD-qp-CONT-TL, spring constant: 0.1N/m, length width thickness: 130400.75m, resonant frequency in air: 32kHz; NanoWorld AG, Neuchtel, Switzerland). During an AST experiment, bacterial nanoscale movements actuate the cantilever to deflect in specific frequencies and amplitudes.
For the development of temperature-controlled experiments with CZA at 37C, we used modular NanoMotion Device (NMD) prototypes. It allowed the reconfiguration of the hardware setup to work with either a standard incubator or a modified measurement head to warm up only the measurement chamber. For the merge of an NMD with a BINDER BD 56 incubator, the size of the incubator fits the entire NMD head with the active vibration damping module, also permitting the user a comfortable manual operation. The incubator shelf was rigid and able to hold the vibration isolator and NMD head (ca. 10kg), and the incubator was modified with an access port to pass through control cables operating the light source, photodetector, and vibration damping module from the outside. Another NMD prototype was equipped with a locally-heated measurement chamber, thermally insulated from the measurement head set-up. A Peltier module as a heating element was installed under the measurement chamber, adapted to temperature control by adding a Pt100 temperature sensor. Temperature was kept at 37C by a Eurotherm EPC3016 PID controller (Eurotherm Ltd, Worthing, United Kingdom) and a custom-made Peltier module driver. Both setups had a temperature stability <0.2C, which is a matching requirement for stable culture conditions.
Each sampled nanomotion signal was split into 10s timeframes. For each timeframe, the linear trend was removed and the variance of the residue frame was estimated. For some experiments, the variance signal was too noisy for classification, necessitating the application of an additional smoothing procedure. A running median with a 1min time window was applied to smooth the variance signal and allow plot interpretation. For the calculation of the SP slope of the variance in the drug phase used for determining the nanomotion dose response in Fig.2b and Supplementary Fig.4, we used the formula log(x)=log(C) + at, where t is time (in min), a is the slope of the common logarithm of the variance trend, and log(C) is the intercept. Variance plots were used here for the visual inspection of results, and are currently the primary tool accessible for investigators. However, more sophisticated SPs are necessary for reliably classifying phenotypes in ASTs.
Nanomotion-based AST was performed using Resistell Phenotech devices (Resistell AG, Muttenz, Switzerland) on a standard laboratory benchtop. Each recording comprises two phases: a 2-h medium phase and a 2-h drug phase. In addition, a short blank phase is conducted to measure the baseline deflections of a new, bare, functionalized cantilever in 50% LB medium for 510min. Raw nanomotion recordings were used to develop classification models using machine learning.
The signal during the blank phase is expected to be constant and primarily flat (variance around 2.6 E-6 or lower). Higher median values or the clear presence of peaks are indicators of potential contamination of the culture medium inside the measurement fluid chamber, sensor manufacturing errors, or an unusual external environmental noise source that should be identified and rectified. In particular, contamination (OD600<0.01) can cause deflection signals that are several orders of magnitude higher than expected for sterile media due to interactions between floating particles in the fluid chamber and the laser beam. The blank phase serves as a quality control but is not used for classification models and, therefore, can be performed several hours prior to recording medium and drug phases.
The medium phase records cantilever deflections after bacterial attachment, showing the oscillations caused by natural bacterial nanomotions stemming from metabolic and cellular activity. Here, variance is expected to be greater (105 to 103) than during the blank phase. The 2-h medium phase duration allows cells to adapt to their new environment within the fluid chamber and generates a baseline that can be compared to bacterial nanomotions during the drug phase. The drug phase measures cellular vibrations after an antibiotic has been introduced to the fluid chamber. The antibiotic is directly pipetted into the medium already present within the measurement chamber.
The Phenotech device detects nanomotion signals resulting from the activity of living cells. However, other sources can create detectable noise during cantilever-based sensing61. Thermal drift occurring on the cantilever62, as well as external sources such as acoustic noise and mechanical vibrations, can all impact measurements. Distinguishing cell-generated vibrations from background noise can be challenging. As such, we employed a supervised machine learning-based approach to extract signal parameters (SPs) containing diagnostic information while minimizing overall background noise. The entire procedure of analyzing motional activity of particles is part of a filed patent (PCT/EP 2023/055596).
First, a batch of initial SPs related to frequency and time domains were extracted, with time and frequency resolution being high to allow for further statistical analysis at this level. Next, different statistical parameters were created with a much coarser time and frequency scale. Finally, various combinations (differences, ratios, etc.) were calculated, forming a final batch of SPs that are more related to antibiotic susceptibility. SPs were estimated for experiments with cells and conditions with well-defined and known outputs (e.g., susceptibility to a given antibiotic could be known through reference AST methods). Here, extracted SPs and outputs formed labeled datasets that could be used for supervised machine learning.
A feature selection algorithm extracted SPs related to the phenomenon of interest. These SPs were selected from the overall batch of SPs to optimize the performance of this so-called machine learning model. In this case, the model was a classifier validated by analysis of metrics measuring the degree of distinguishing antibiotic susceptibility. Therefore, a forward selection method was applied. All SPs were subsequently evaluated in the classifier with repeated stratified cross-validation. The SPs that enabled the classifier to reach maximal accuracy were added to the stack of selected SPs and deleted from the remaining SPs. In the next iteration, all remaining SPs were again tested with the already-selected SPs. The best-performing SP was again added to the selected SP stack. This process was repeated several times until the overall performance reached a plateau or a predefined number of SPs were selected. In the final model (iii), these newly found SPs were then used as machine learning model features. Classifier models were trained using the complete available dataset and could now be used to classify previously unseen data. The Supplementary information elaborates in more detail on that process and lists all SPs used in the different classification models.
After achieving Pareto optimality, the models were tested on independent test datasets consisting exclusively of strains of K. pneumoniae or E. coli that were not used in the training of the corresponding model. We used either spiked blood cultures or directly anonymized remnant PBC from the Lausanne University Hospital (CHUV) in Lausanne. Spiking was predominantly utilized to increase the fraction of resistant strains to obtain more representative specificity (classification performance of resistant strains), as resistance rates at that hospital are around 10 % for CRO and CIP and close to non-existent for CZA. Each nanomotion recording was classified separately and combined using the median to a sample reporting accuracy, sensitivity and specificity exactly as described for reporting the training performance.
In addition to this, we performed an interim analysis of the multicentric clinical performance study PHENOTECH-1 (NCT05613322), conducted in Switzerland (Lausanne University Hospital, Lausanne), Spain (University Hospital Ramn y Cajal, Madrid) and Austria (Medical University of Innsbruck, Innsbruck). The study evaluates the performance of the nanomotion AST with the Phenotech device using the CRO model on E. coli and K. pneumoniae from fresh residual PBC. Ethical review and approval were obtained by the hospital ethics committee at each participating site. In Lausanne and Innsbruck, only samples from patients who had previously agreed to the use of their residual biological material were utilized. In Madrid, consent for participation was not required for this study in accordance with institutional requirements. No compensation was paid to participants. The interim results reported here comprise the first included 85 samples with complete data entry. The eventual sample size of 250 was estimated based on the expected rate of E. coli and K. pneumoniae samples susceptible to the antibiotic in the three countries (i.e., 80%). Allowing for up to 10% samples with missing data or technical errors, an overall sample size of 250 would include 180 truly susceptible samples with 98% power to demonstrate that sensitivity is at least 90%. The PHENOTECH-1 study is expected to conclude in 2024. The endpoints of this study include the accuracy, sensitivity, and specificity of the device according to ISO-20776-2 (2021), as well as the time to result from the start of the AST to the generation of the result in form of a time stamped report. Regarding inclusion criteria, patients aged 18 years or older, with positive blood cultures for either E. coli or K. pneumoniae, are eligible for participation in the study. Additionally, Phenotech AST needs to be performed within 24h of the blood culture turning positive. Patients with polymicrobial samples are excluded from the study.
Qualitative results of the Kirby Bauer disk diffusion assay, i.e., either R or S, were used for benchmarking. Clinical breakpoints for the class definition were according to EUCAST in 2022. The samples coming from one PBC were measured in technical triplicates for 4h. The results from each recording were automatically combined to a sample. Instead of the median score, a majority voting system was in place that is, RRR, RRS and RR- return predicted resistance, SSS, SSR, SS- return susceptibility. In this way even if one recording needed to be excluded because of technical errors, or detection of substantial elongation of the specimen, the sample could be interpretated. Only if two or more recordings were excluded, or the exclusion of one recording resulted in the disagreement between the two remaining recordings, the sample would be classified as non-conclusive. The experiments were not randomized and the investigators were unblinded during experiments and outcome assessment. Information on sex, gender, and age of participants was not collected in this study as having no impact on the generalizability and translation of the findings. At the time of analysis, the data set included 119 samples, of which 12 screening failures, 5 with technical errors or elongation, and 12 incomplete/unverified. Samples with complete, verified and cleaned data accounted to 90. Of these, the first 85 samples were selected of which 20 samples derived from CHUV, 48 samples from Ramon y Cajal Hospital and 17 samples from Medical University of Innsbruck.
Statistical details can be found in the figure legends. Data are presented as mean or medianSD or representative single experiments and provided in the Source data file. In Figs.3, 5, and 6, the performance calculation is based on single recordings for which a score was calculated. Each recording is depicted as a datapoint representing a biological replicate originating from a different PBC. Performance calculation in Fig.4 is based on the median of the scores calculated for each technical replicate originating from the same PBC. Thus, each datapoint represents the median score as it is currently implemented in the PHENOTECH-1 clinical performance study. In each case, scores are logits predicted by the corresponding logistic regression model. In Fig.5e the two-tailed MannWhitney U test was performed for calculating a p-value. Statistical analysis and graphs were generated with GraphPad Prism 10.
Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.
Continued here:
Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform - Nature.com
- What Is Machine Learning? | How It Works, Techniques ... [Last Updated On: September 5th, 2019] [Originally Added On: September 5th, 2019]
- Start Here with Machine Learning [Last Updated On: September 22nd, 2019] [Originally Added On: September 22nd, 2019]
- What is Machine Learning? | Emerj [Last Updated On: October 1st, 2019] [Originally Added On: October 1st, 2019]
- Microsoft Azure Machine Learning Studio [Last Updated On: October 1st, 2019] [Originally Added On: October 1st, 2019]
- Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning | Simplilearn [Last Updated On: October 1st, 2019] [Originally Added On: October 1st, 2019]
- What is Machine Learning? A definition - Expert System [Last Updated On: October 2nd, 2019] [Originally Added On: October 2nd, 2019]
- Machine Learning | Stanford Online [Last Updated On: October 2nd, 2019] [Originally Added On: October 2nd, 2019]
- How to Learn Machine Learning, The Self-Starter Way [Last Updated On: October 17th, 2019] [Originally Added On: October 17th, 2019]
- definition - What is machine learning? - Stack Overflow [Last Updated On: November 3rd, 2019] [Originally Added On: November 3rd, 2019]
- Artificial Intelligence vs. Machine Learning vs. Deep ... [Last Updated On: November 3rd, 2019] [Originally Added On: November 3rd, 2019]
- Machine Learning in R for beginners (article) - DataCamp [Last Updated On: November 3rd, 2019] [Originally Added On: November 3rd, 2019]
- Machine Learning | Udacity [Last Updated On: November 3rd, 2019] [Originally Added On: November 3rd, 2019]
- Machine Learning Artificial Intelligence | McAfee [Last Updated On: November 3rd, 2019] [Originally Added On: November 3rd, 2019]
- Machine Learning [Last Updated On: November 3rd, 2019] [Originally Added On: November 3rd, 2019]
- AI-based ML algorithms could increase detection of undiagnosed AF - Cardiac Rhythm News [Last Updated On: November 19th, 2019] [Originally Added On: November 19th, 2019]
- The Cerebras CS-1 computes deep learning AI problems by being bigger, bigger, and bigger than any other chip - TechCrunch [Last Updated On: November 19th, 2019] [Originally Added On: November 19th, 2019]
- Can the planet really afford the exorbitant power demands of machine learning? - The Guardian [Last Updated On: November 19th, 2019] [Originally Added On: November 19th, 2019]
- New InfiniteIO Platform Reduces Latency and Accelerates Performance for Machine Learning, AI and Analytics - Business Wire [Last Updated On: November 19th, 2019] [Originally Added On: November 19th, 2019]
- How to Use Machine Learning to Drive Real Value - eWeek [Last Updated On: November 19th, 2019] [Originally Added On: November 19th, 2019]
- Machine Learning As A Service Market to Soar from End-use Industries and Push Revenues in the 2025 - Downey Magazine [Last Updated On: November 26th, 2019] [Originally Added On: November 26th, 2019]
- Rad AI Raises $4M to Automate Repetitive Tasks for Radiologists Through Machine Learning - - HIT Consultant [Last Updated On: November 26th, 2019] [Originally Added On: November 26th, 2019]
- Machine Learning Improves Performance of the Advanced Light Source - Machine Design [Last Updated On: November 26th, 2019] [Originally Added On: November 26th, 2019]
- Synthetic Data: The Diamonds of Machine Learning - TDWI [Last Updated On: November 26th, 2019] [Originally Added On: November 26th, 2019]
- The transformation of healthcare with AI and machine learning - ITProPortal [Last Updated On: November 26th, 2019] [Originally Added On: November 26th, 2019]
- Workday talks machine learning and the future of human capital management - ZDNet [Last Updated On: November 26th, 2019] [Originally Added On: November 26th, 2019]
- Machine Learning with R, Third Edition - Free Sample Chapters - Neowin [Last Updated On: November 26th, 2019] [Originally Added On: November 26th, 2019]
- Verification In The Era Of Autonomous Driving, Artificial Intelligence And Machine Learning - SemiEngineering [Last Updated On: November 26th, 2019] [Originally Added On: November 26th, 2019]
- Podcast: How artificial intelligence, machine learning can help us realize the value of all that genetic data we're collecting - Genetic Literacy... [Last Updated On: November 28th, 2019] [Originally Added On: November 28th, 2019]
- The Real Reason Your School Avoids Machine Learning - The Tech Edvocate [Last Updated On: November 28th, 2019] [Originally Added On: November 28th, 2019]
- Siri, Tell Fido To Stop Barking: What's Machine Learning, And What's The Future Of It? - 90.5 WESA [Last Updated On: November 28th, 2019] [Originally Added On: November 28th, 2019]
- Microsoft reveals how it caught mutating Monero mining malware with machine learning - The Next Web [Last Updated On: November 28th, 2019] [Originally Added On: November 28th, 2019]
- The role of machine learning in IT service management - ITProPortal [Last Updated On: November 28th, 2019] [Originally Added On: November 28th, 2019]
- Global Director of Tech Exploration Discusses Artificial Intelligence and Machine Learning at Anheuser-Busch InBev - Seton Hall University News &... [Last Updated On: November 28th, 2019] [Originally Added On: November 28th, 2019]
- The 10 Hottest AI And Machine Learning Startups Of 2019 - CRN: The Biggest Tech News For Partners And The IT Channel [Last Updated On: November 28th, 2019] [Originally Added On: November 28th, 2019]
- Startup jobs of the week: Marketing Communications Specialist, Oracle Architect, Machine Learning Scientist - BetaKit [Last Updated On: November 30th, 2019] [Originally Added On: November 30th, 2019]
- Here's why machine learning is critical to success for banks of the future - Tech Wire Asia [Last Updated On: December 2nd, 2019] [Originally Added On: December 2nd, 2019]
- 3 questions to ask before investing in machine learning for pop health - Healthcare IT News [Last Updated On: December 8th, 2019] [Originally Added On: December 8th, 2019]
- Machine Learning Answers: If Caterpillar Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 8th, 2019] [Originally Added On: December 8th, 2019]
- Measuring Employee Engagement with A.I. and Machine Learning - Dice Insights [Last Updated On: December 8th, 2019] [Originally Added On: December 8th, 2019]
- Amazon Wants to Teach You Machine Learning Through Music? - Dice Insights [Last Updated On: December 8th, 2019] [Originally Added On: December 8th, 2019]
- Machine Learning Answers: If Nvidia Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 8th, 2019] [Originally Added On: December 8th, 2019]
- AI and machine learning platforms will start to challenge conventional thinking - CRN.in [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- Machine Learning Answers: If Twitter Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- Machine Learning Answers: If Seagate Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- Machine Learning Answers: If BlackBerry Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- Amazon Releases A New Tool To Improve Machine Learning Processes - Forbes [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- Another free web course to gain machine-learning skills (thanks, Finland), NIST probes 'racist' face-recog and more - The Register [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- Kubernetes and containers are the perfect fit for machine learning - JAXenter [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- TinyML as a Service and machine learning at the edge - Ericsson [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- AI and machine learning products - Cloud AI | Google Cloud [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- Machine Learning | Blog | Microsoft Azure [Last Updated On: December 23rd, 2019] [Originally Added On: December 23rd, 2019]
- Machine Learning in 2019 Was About Balancing Privacy and Progress - ITPro Today [Last Updated On: December 25th, 2019] [Originally Added On: December 25th, 2019]
- CMSWire's Top 10 AI and Machine Learning Articles of 2019 - CMSWire [Last Updated On: December 25th, 2019] [Originally Added On: December 25th, 2019]
- Here's why digital marketing is as lucrative a career as data science and machine learning - Business Insider India [Last Updated On: January 13th, 2020] [Originally Added On: January 13th, 2020]
- Dell's Latitude 9510 shakes up corporate laptops with 5G, machine learning, and thin bezels - PCWorld [Last Updated On: January 13th, 2020] [Originally Added On: January 13th, 2020]
- Finally, a good use for AI: Machine-learning tool guesstimates how well your code will run on a CPU core - The Register [Last Updated On: January 13th, 2020] [Originally Added On: January 13th, 2020]
- Cloud as the enabler of AI's competitive advantage - Finextra [Last Updated On: January 13th, 2020] [Originally Added On: January 13th, 2020]
- Forget Machine Learning, Constraint Solvers are What the Enterprise Needs - - RTInsights [Last Updated On: January 13th, 2020] [Originally Added On: January 13th, 2020]
- Informed decisions through machine learning will keep it afloat & going - Sea News [Last Updated On: January 13th, 2020] [Originally Added On: January 13th, 2020]
- The Problem with Hiring Algorithms - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times [Last Updated On: January 13th, 2020] [Originally Added On: January 13th, 2020]
- New Program Supports Machine Learning in the Chemical Sciences and Engineering - Newswise [Last Updated On: January 13th, 2020] [Originally Added On: January 13th, 2020]
- AI-System Flags the Under-Vaccinated in Israel - PrecisionVaccinations [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- New Contest: Train All The Things - Hackaday [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- AFTAs 2019: Best New Technology Introduced Over the Last 12 MonthsAI, Machine Learning and AnalyticsActiveViam - www.waterstechnology.com [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- Educate Yourself on Machine Learning at this Las Vegas Event - Small Business Trends [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- Seton Hall Announces New Courses in Text Mining and Machine Learning - Seton Hall University News & Events [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- Looking at the most significant benefits of machine learning for software testing - The Burn-In [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- Leveraging AI and Machine Learning to Advance Interoperability in Healthcare - - HIT Consultant [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- Adventures With Artificial Intelligence and Machine Learning - Toolbox [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- Five Reasons to Go to Machine Learning Week 2020 - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- Uncover the Possibilities of AI and Machine Learning With This Bundle - Interesting Engineering [Last Updated On: January 22nd, 2020] [Originally Added On: January 22nd, 2020]
- Learning that Targets Millennial and Generation Z - HR Exchange Network [Last Updated On: January 23rd, 2020] [Originally Added On: January 23rd, 2020]
- Red Hat Survey Shows Hybrid Cloud, AI and Machine Learning are the Focus of Enterprises - Computer Business Review [Last Updated On: January 23rd, 2020] [Originally Added On: January 23rd, 2020]
- Vectorspace AI Datasets are Now Available to Power Machine Learning (ML) and Artificial Intelligence (AI) Systems in Collaboration with Elastic -... [Last Updated On: January 23rd, 2020] [Originally Added On: January 23rd, 2020]
- What is Machine Learning? | Types of Machine Learning ... [Last Updated On: January 23rd, 2020] [Originally Added On: January 23rd, 2020]
- How Machine Learning Will Lead to Better Maps - Popular Mechanics [Last Updated On: January 30th, 2020] [Originally Added On: January 30th, 2020]
- Jenkins Creator Launches Startup To Speed Software Testing with Machine Learning -- ADTmag - ADT Magazine [Last Updated On: January 30th, 2020] [Originally Added On: January 30th, 2020]
- An Open Source Alternative to AWS SageMaker - Datanami [Last Updated On: January 30th, 2020] [Originally Added On: January 30th, 2020]
- Machine Learning Could Aid Diagnosis of Barrett's Esophagus, Avoid Invasive Testing - Medical Bag [Last Updated On: January 30th, 2020] [Originally Added On: January 30th, 2020]
- OReilly and Formulatedby Unveil the Smart Cities & Mobility Ecosystems Conference - Yahoo Finance [Last Updated On: January 30th, 2020] [Originally Added On: January 30th, 2020]