Quantification of Global Tortuosity in Retinal Blood Vessels

Tortuosity is a parameter that indicates the tendency of a blood vessel segment to contain multiple twists and turns. Chronic hemodynamic changes in the body due to diabetes and hypertension will manifest as increased retinal vascular tortuosity, rendering tortuosity as a suitable indicator for diabetic and hypertensive retinopathy. Retinal tortuosity may be evaluated locally on a single segment or globally in the complete vascular network. Global tortuosity quantification consists of automated segmentation and partition of retinal vessel network, local tortuosity measurement, and global tortuosity index derivation from weighted combination of local tortuosity values. This paper proposes several weighting schemes and evaluates their performance when combined with different local tortuosity indexes. We perform rank correlation analysis to find the global tortuosity quantification that is most consistent with the ophthalmologists. Our results show that local tortuosity indexes that are robust to variations in scale and number of sampling points provide the best performance. Furthermore, weighting scheme based on chord length yields better results than the one based on arc length. The combination of Tortuosity Density (TD) local index and Tortuosity Density Global (TDG) weighting scheme provides the highest consistency with ophthalmologists, with the average rank correlation coefficient of 0.98 (p-value < 0.03)

Reproducibility of Standing Posture for X-Ray Radiography: A Feasibility Study of the BalancAid with Healthy Young Subjects

Unreliable spinal X-ray radiography measurement due to standing postural variability can be minimized by using positional supports. In this study, we introduce a balancing device, named BalancAid, to position the patients in a reproducible position during spinal X-ray radiography. This study aimed to investigate the performance of healthy young subjects’ standing posture on the BalancAid compared to standing on the ground mimicking the standard X-rays posture in producing a reproducible posture for the spinal X-ray radiography. A study on the posture reproducibility measurement was performed by taking photographs of 20 healthy young subjects with good balance control standing on the BalancAid and the ground repeatedly within two consecutive days. We analyzed nine posterior–anterior (PA) and three lateral (LA) angles between lines through body marks placed in the positions of T3, T7, T12, L4 of the spine to confirm any translocations and movements between the first and second day measurements. No body marks repositioning was performed to avoid any error. Lin’s CCC test on all angles comparing both standing postures demonstrated that seven out of nine angles in PA view, and two out of three angles in LA view gave better reproducibility for standing on the BalancAid compared to standing on the ground. The PA angles concordance is on average better than that of the LA angles

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data.

Early detection of breast cancer can be achieved through mutation detection in DNA sequences, which can be acquired through patient blood samples. Mutation detection can be performed using alignment and machine learning techniques. However, alignment techniques require reference sequences, and machine learning techniques still cannot predict index mutation and require supporting tools. Therefore, in this research, a Temporal Convolutional Network (TCN) model was proposed to detect the type and index mutation faster and without reference sequences and supporting tools. The architecture of the proposed TCN model is specifically designed for sequential labeling tasks on DNA sequence data. This allows for the detection of the mutation type of each nucleotide in the sequence, and if the nucleotide has a mutation, the index mutation can be obtained. The proposed model also uses 2-mers and 3-mers mapping techniques to improve detection performance. Based on the tests that have been carried out, the proposed TCN model can achieve the highest F1-score of 0.9443 for COSMIC dataset and 0.9629 for RSCM dataset, Additionally, the proposed TCN model can detect index mutation six times faster than BiLSTM model. Furthermore, the proposed model can detect type and index mutations based on the patient's DNA sequence, without the need for reference sequences or other additional tools

Automatic detection system of cervical cancer cells using color intensity classification

The conventional Pap smear has been undeniably responsible in reducing the number of incidence and mortality of cervical cancer. However, few concerns have arisen such as the shortage of skilled and experienced pathologists and the increasing workload as a result of more individuals having gained access to preventive health care which eventually will make the reviewing procedure becomes time consuming and highly prone to human errors. In order to solve this problem, an automated detection system of cervical cancer cells has been developed. The detection of cervical cancer cells is based on the morphology of the cells and level set operations. Test result shows, that by using color intensity classification the system is able to differentiate between normal and cancerous cells. This system will hopefully help the pathologist to reduce the work-load and minimize human error while maintaining and improving the accuracy of the system