[Artificial intelligence empowers laboratory medicine in Industry 4.0].

Since 2017, China, the United States, and the European Union have successively issued national-level artificial intelligence (AI) strategic development plans, and the human history is about to witness the 4th industrial revolution with the theme of "intelligence". In the field of medical testing, the explosive growth of AI theories and technologies also provide a new direction for the development of medical testing theory, methods and applications. We review the evolution of AI and the recent progress in three major elements of AI, namely algorithms, data and computing power, and elaborate on the combined innovation of "AI + testing" in light of the key application dimensions of medical testing. The major applications include specimen collection robots, sample dilution robots and sample transfer robots involved in the processing of test specimens; test item mining such as tumor markers and pharmacogenomics; cytomorphology, laboratory medicine data processing, auxiliary diagnostic models, and internet-based medical tests. With the advent of the era of Industry 4.0, AI technology will promote the development of medical testing from automation to a highly intelligent stage.

Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network.

OBJECTIVES: To evaluate the application of a deep learning architecture, based on the convolutional neural network (CNN) technique, to perform automatic tumor segmentation of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: In this prospective study, 87 MRI containing tumor regions were acquired from newly diagnosed NPC patients. These 87 MRI were augmented to >60,000 images. The proposed CNN network is composed of two phases: feature representation and scores map reconstruction. We designed a stepwise scheme to train our CNN network. To evaluate the performance of our method, we used case-by-case leave-one-out cross-validation (LOOCV). The ground truth of tumor contouring was acquired by the consensus of two experienced radiologists. RESULTS: The mean values of dice similarity coefficient, percent match, and their corresponding ratio with our method were 0.89±0.05, 0.90±0.04, and 0.84±0.06, respectively, all of which were better than reported values in the similar studies. CONCLUSIONS: We successfully established a segmentation method for NPC based on deep learning in contrast-enhanced magnetic resonance imaging. Further clinical trials with dedicated algorithms are warranted.

Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study.

Purpose: In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods: PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results: A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P < 0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion: A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.

Metabonomics screening of serum identifies pyroglutamate as a diagnostic biomarker for nonalcoholic steatohepatitis.

OBJECTIVE: A key step in managing non-alcoholic fatty liver disease (NAFLD) is to differentiate nonalcoholic steatohepatitis (NASH) from simple steatosis (SS). METHOD: Serum samples were collected from three groups: NASH patients (N=21), SS patients (N=38) and healthy controls (N=31). High performance liquid chromatography-mass spectrometry (HPLC-MS) was used to analyse the metabolic profile of the serum samples. The acquired data were processed by multivariate principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) to identify novel metabolites. The potential biomarkers were quantitatively determined and their diagnostic power was further validated. RESULTS: A total of 56 metabolites were capable of distinguishing NASH from SS samples based on the OPLS-DA model. Pyroglutamate was found to be the most promising factor in distinguishing the NASH from SS groups. With an optimal cut-off value of 4.82mmol/L, the sensitivity and specificity of the diagnosis of NASH were 72% and 85%, respectively. The area under the receiver operating characteristic (AUROC) of the pyroglutamate levels of NASH versus SS patients was more than those of tumor necrosis factor-α, adiponectin and interleukin-8. CONCLUSION: These data suggest that pyroglutamate may be a new and useful biomarker for the diagnosis of NASH.

[Design of acoustic radiation force module for ultrasound elastography].

Developing an acoustic radiation force excitation module including 64 channels based in FPGA for ultrasound elastography. The circuit of the module was derived in bipolar, and the parameters such as excitation frequency, pulse repetition frequency, pulse number, element number and focus depth were adjustable. The acoustic field for special parameter was experimented with OptiSon laser acoustic field system with a result which reflects the width of focal spot is about 3 mm. The acoustic power was experimented with RFB2000 radiation force balance with a result which reflects acoustic power is increasing linearly with the number of pulses and the number of elements, and is increasing squarely with the peak-to-peak value of excitation voltage. The module is promising in factual application which can be triggered externally in synchronously, and can be combined with B-mode ultrasound system for ultrasound elastography.