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General General

DiatomNet v1.0: A novel approach for automatic diatom testing for drowning diagnosis in forensically biomedical application.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Diatom testing is supportive for drowning diagnosis in forensic medicine. However, it is very time-consuming and labor-intensive for technicians to identify microscopically a handful of diatoms in sample smears, especially under complex observable backgrounds. Recently, we successfully developed a software, named DiatomNet v1.0 intended to automatically identify diatom frustules in a whole slide under a clear background. Here, we introduced this new software and performed a validation study to elucidate how DiatomNet v1.0 improved its performance with the influence of visible impurities.

METHODS : DiatomNet v1.0 has an intuitive, user-friendly and easy-to-learn graphical user interface (GUI) built in the Drupal and its core architecture for slide analysis including a convolutional neural network (CNN) is written in Python language. The build-in CNN model was evaluated for diatom identification under very complex observable backgrounds with mixtures of common impurities, including carbon pigments and sand sediments. Compared to the original model, the enhanced model following optimization with limited new datasets was evaluated systematically by independent testing and random control trials (RCTs).

RESULTS : In independent testing, the original DiatomNet v1.0 was moderately affected, especially when higher densities of impurities existed, and achieved a low recall of 0.817 and F1 score of 0.858 but good precision of 0.905. Following transfer learning with limited new datasets, the enhanced version had better results, with recall and F1 score values of 0.968. A comparative study on real slides showed that the upgraded DiatomNet v1.0 obtained F1 scores of 0.86 and 0.84 for carbon pigment and sand sediment, respectively, slightly worse than manual identification (carbon pigment: 0.91; sand sediment: 0.86), but much less time was needed.

CONCLUSIONS : The study verified that forensic diatom testing with aid of DiatomNet v1.0 is much more efficient than traditionally manual identification even under complex observable backgrounds. In terms of forensic diatom testing, we proposed a suggested standard on build-in model optimization and evaluation to strengthen the software's generalization in potentially complex conditions.

Zhang Ji, Vieira Duarte Nuno, Cheng Qi, Zhu Yongzheng, Deng Kaifei, Zhang Jianhua, Qin Zhiqiang, Sun Qiran, Zhang Tianye, Ma Kaijun, Zhang Xiaofeng, Huang Ping

2023-Feb-21

Artificial intelligence, Convolutional neural network, Deep learning, Diatom testing, Drowning

General General

Mobile app for targeted selective treatment of haemonchosis in sheep.

In Veterinary parasitology ; h5-index 43.0

Livestock is an important part of many countries gross domestic product, and sanitary control impacts herd management costs. To contribute to incorporating new technologies into this economic chain, this work presents a mobile application for decision assistance to treatment against parasitic infection by Haemonchus contortus in small ruminants. Based on the Android system, the proposed software is a semi-automated computer-aided procedure to assist Famacha© pre-trained farmers in applying anthelmintic treatment. It mimics the two-class decision procedure performed by the veterinarian with the help of the Famacha© card. The embedded cell phone camera was employed to acquire an image from the ocular conjunctival mucosa, classifying the animal as healthy or anemic. Two machine-learning strategies were assessed, resulting in an accuracy of 83 % for a neural network and 87 % for a support vector machine (SVM). The SVM classifier was embedded into the app and made available for evaluation. This work is particularly interesting to small property owners from regions with difficult access or restrictions on obtaining continuous post-training technical guidance to use the Famacha© method effectively.

de Souza Lucas Fiamoncini, Costa Márcio Holsbach, Riet-Correa Beatriz

2023-Feb-28

Classifier, Famacha, Machine learning, Parasites, Small ruminants

General General

Predicting mechanical properties of silk from its amino acid sequences via machine learning.

In Journal of the mechanical behavior of biomedical materials ; h5-index 50.0

The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, is that their mechanical properties are significantly dependent on the amino acid sequence. Numerous studies have been conducted to determine the specific relationship between the amino acid sequence of silk and its mechanical properties. Still, the relationship between the amino acid sequence of silk and its mechanical properties is yet to be clarified. Other fields have adopted machine learning (ML) to establish a relationship between the inputs, such as the ratio of different input material compositions and the resulting mechanical properties. We have proposed a method to convert the amino acid sequence into numerical values for input and succeeded in predicting the mechanical properties of silk from its amino acid sequences. Our study sheds light on predicting mechanical properties of silk fiber from respective amino acid sequences.

Kim Yoonjung, Yoon Taeyoung, Park Woo B, Na Sungsoo

2023-Feb-22

Machine learning, Mechanical characterization, Sequence analysis, Silk fiber

Surgery Surgery

Automatic grading of patients with a unilateral facial paralysis based on the Sunnybrook Facial Grading System - A deep learning study based on a convolutional neural network.

In American journal of otolaryngology ; h5-index 23.0

PURPOSE : In order to assess the severity and the progression of a unilateral peripheral facial palsy the Sunnybrook Facial Grading System (SFGS) is a well-established grading system due to its clinical relevance, sensitivity, and robust measuring method. However, training is required in order to achieve a high inter-rater reliability. This study investigated the automated grading of facial palsy patients based on the SFGS using a convolutional neural network.

METHODS : A total of 116 patients with a unilateral peripheral facial palsy and 9 healthy subjects were recorded performing the Sunnybrook poses. A separate model was trained for each of the 13 elements of the SFGS and then used to calculate the Sunnybrook subscores and composite score. The performance of the automated grading system was compared to three clinicians experienced in the grading of a facial palsy.

RESULTS : The inter-rater reliability of the convolutional neural network was within the range of human observers, with an average intra-class correlation coefficient of 0.87 for the composite Sunnybrook score, 0.45 for the resting symmetry subscore, 0.89 for the symmetry of voluntary movement subscore, and 0.77 for the synkinesis subscore.

CONCLUSIONS : This study showed the potential of the automated SFGS to be implemented in a clinical setting. The automated grading system adhered to the original SFGS, which makes the implementation and interpretation of the automated grading more straightforward. The automated system can be implemented in numerous settings such as online consults in an e-Health environment, since the model used 2D images captured from a video recording.

Ten Harkel Timen C, de Jong Guido, Marres Henri A M, Ingels Koen J A O, Speksnijder Caroline M, Maal Thomas J J

2023-Feb-25

Convolutional neural network, Deep learning, Facial paralysis, Machine learning, Medical imaging, Sunnybrook facial grading system

General General

Characterization of Calculus bovis by principal component analysis assisted qHNMR profiling to distinguish nefarious frauds.

In Journal of pharmaceutical and biomedical analysis

A new approach is developed for the reliable classification of Calculus bovis along with the identification of willfully contaminated C. bovis species and the quantification of unclaimed adulterants. Guided by a principal component analysis, NMR data mining achieved a near-holistic chemical characterization of three types of authenticated C. bovis, including natural C. bovis (NCB), in vitro cultured C. bovis (Ivt-CCB), and artificial C. bovis (ACB). In addition, species-specific markers used for quality evaluation and species classification were confirmed. That is, the content of taurine in NCB is near negligible, while choline and hyodeoxycholic acid are characteristic for identifying Ivt-CCB and ACB, respectively. Besides, the peak shapes and chemical shifts of H2-25 of glycocholic acid could assist in the recognition of the origins of C. bovis. Based on these discoveries, a set of commercial NCB samples, macroscopically identified as problematic species, was examined with deliberately added sugars and outliers discovered. Absolute quantification of the identified sugars was realized by qHNMR using a single, nonidentical internal calibrant (IC). This study represents the first systematic study of C. bovis metabolomics via an NMR-driven methodology, which advances the toolbox for quality control of TCM and provides a more definitive reference point for future chemical and biological studies of C. bovis as a valuable materia medica.

Tang Yu, Han Zhu, Zhang Han, Che Li, Liao Genjie, Peng Jun, Lin Yu, Wang Yi

2023-Mar-01

Absolute quantification, Adulteration, Calculus bovis, Quality control, qHNMR

Radiology Radiology

Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.

In Lung cancer (Amsterdam, Netherlands)

OBJECTIVES : The aim of this study was to differentiate benign from malignant tumors in the anterior mediastinum based on computed tomography (CT) imaging characteristics, which could be useful in preoperative planning. Additionally, our secondary aim was to differentiate thymoma from thymic carcinoma, which could guide the use of neoadjuvant therapy.

MATERIALS AND METHODS : Patients referred for thymectomy were retrospectively selected from our database. Twenty-five conventional characteristics were evaluated by visual analysis, and 101 radiomic features were extracted from each CT. In the step of model training, we applied support vector machines to train classification models. Model performance was assessed using the area under the receiver operating curves (AUC).

RESULTS : Our final study sample comprised 239 patients, 59 (24.7 %) with benign mediastinal lesions and 180 (75.3 %) with malignant thymic tumors. Among the malignant masses, there were 140 (58.6 %) thymomas, 23 (9.6 %) thymic carcinomas, and 17 (7.1 %) non-thymic lesions. For the benign versus malignant differentiation, the model that integrated both conventional and radiomic features achieved the highest diagnostic performance (AUC = 0.715), in comparison to the conventional (AUC = 0.605) and radiomic-only (AUC = 0.678) models. Similarly, regarding thymoma versus thymic carcinoma differentiation, the model that integrated both conventional and radiomic features also achieved the highest diagnostic performance (AUC = 0.810), in comparison to the conventional (AUC = 0.558) and radiomic-only (AUC = 0.774) models.

CONCLUSION : CT-based conventional and radiomic features with machine learning analysis could be useful for predicting pathologic diagnoses of anterior mediastinal masses. The diagnostic performance was moderate for differentiating benign from malignant lesions and good for differentiating thymomas from thymic carcinomas. The best diagnostic performance was achieved when both conventional and radiomic features were integrated in the machine learning algorithms.

Mayoral Maria, Pagano Andrew M, Araujo-Filho Jose Arimateia Batista, Zheng Junting, Perez-Johnston Rocio, Tan Kay See, Gibbs Peter, Fernandes Shepherd Annemarie, Rimner Andreas, Simone Ii Charles B, Riely Gregory, Huang James, Ginsberg Michelle S

2023-Feb-21

Artificial intelligence, Computed tomography, Machine learning, Radiomics, Thymic epithelial tumors