What are the novel findings of this work?In this study, we developed and validated an artificial intelligence system, the Prenatal ultrasound diagnosis Artificial Intelligence Conduct System (PAICS), to detect nine specific intracranial-malformation patterns in standard sonographic reference planes of the fetal central nervous system (CNS). The PAICS achieved excellent performance on both internal and external validation, with accuracy comparable to that of expert sonologists, while requiring significantly less time.
What are the clinical implications of this work?The PAICS is a real-time artificial intelligence-aided image recognition system capable of detecting fetal intracranial malformations. This fast, accurate algorithm has the potential to be an effective and efficient tool in screening for congenital CNS malformations. It should be particularly useful in community hospitals, which often lack imaging expertise.
For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio, it is difficult to achieve high discrimination accuracy by using the miniature Raman spectrometer. Therefore, a pattern recognition method of the adaptive net analyte signal (NAS) weight k-local hyperplane (ANWKH) is proposed to increase the classification accuracy. ANWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN), and combines the advantages of the adaptive weight k-local hyperplane (AWKH) and the net analyte signal (NAS). In this algorithm, NAS was first used to eliminate the influence caused by other non-target factors. Then, the distance between the test set samples and hyperplane was calculated with consideration of the feature weights. The HKNN only works well for small values of the nearest-neighbor. However, the accuracy decreases with increasing values of the nearest-neighbor. The method presented in this paper can resolve the basic shortcoming by using the feature weights. The original spectra are projected into the vertical subspace without the objective factors. NAS was employed to obtain the spectra without irrelevant information. NAS can improve the classification accuracy, sensitivity, and specificity of breast cancer early diagnosis. Experimental results of Raman spectra detection in vitro of breast tissues showed that the proposed algorithm can obtain high classification accuracy, sensitivity, and specificity. This paper demonstrates that the ANWKH algorithm is feasible for early clinical diagnosis of breast cancer in the future.
Self-piercing riveting (SPR) has become an important alternative joining technique for the automotive applications of aluminum sheets. Most existing SPR machines use electrical motors to drive a rivet into the sheets. A significant amount of research has been conducted to improve an SPR joint’s strength by increasing the mechanical interlock. In this paper, a new process is presented using gunpowder to drive the riveting process. A joint formed using the new process has different geometric characteristics from one created using a conventional system. The tensile-shear, cross-tension, fatigue, and impact performances of self-piercing riveted joints using the new device are compared to those of spot-welded joints on aluminum sheets. The experiment has proven that the new SPR joints have provided a similar or higher strength than resistance spot welds.
Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%.
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