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Extraction of dynamic features from hand drawn data for the identification of children with handwriting difficulty

Khalid PI, Yunus J, Adnan R

Res Dev Disabil, 2010 Jan-Feb;31(1):256-62.
PMID: 19854613 DOI: 10.1016/j.ridd.2009.09.009

Studies have shown that differences between children with and without handwriting difficulties lie not only in the written product (static data) but also in dynamic data of handwriting process. Since writing system varies among countries and individuals, this study was conducted to determine the feasibility of using quantitative outcome measures of children's drawing to identify children who are at risk of handwriting difficulties. A sample of 143 first graders of a normal primary school was investigated regarding their handwriting ability. The children were divided into two groups: test and control. Ten children from test group and 40 children from control group were individually tested for their Visual Motor Integration skills. Analysis on dynamic data indicated significant differences between the two groups in temporal and spatial measures of the drawing task performance. Thus, kinematic analysis of children's drawing is feasible to provide performance characteristic of handwriting ability, supporting its use in screening for handwriting difficulty.

Matched MeSH terms: Diagnosis, Computer-Assisted
Fulltext Diagnosis of Lung Cancer by Fractal Analysis of Damaged DNA

Namazi H, Kiminezhadmalaie M

Comput Math Methods Med, 2015;2015:242695.
PMID: 26539245 DOI: 10.1155/2015/242695

Cancer starts when cells in a part of the body start to grow out of control. In fact cells become cancer cells because of DNA damage. A DNA walk of a genome represents how the frequency of each nucleotide of a pairing nucleotide couple changes locally. In this research in order to study the cancer genes, DNA walk plots of genomes of patients with lung cancer were generated using a program written in MATLAB language. The data so obtained was checked for fractal property by computing the fractal dimension using a program written in MATLAB. Also, the correlation of damaged DNA was studied using the Hurst exponent measure. We have found that the damaged DNA sequences are exhibiting higher degree of fractality and less correlation compared with normal DNA sequences. So we confirmed this method can be used for early detection of lung cancer. The method introduced in this research not only is useful for diagnosis of lung cancer but also can be applied for detection and growth analysis of different types of cancers.

Matched MeSH terms: Diagnosis, Computer-Assisted
Objective assessment of psoriasis erythema for PASI scoring

Ahmad Fadzil MH, Ihtatho D, Affandi AM, Hussein SH

Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:4070-3.
PMID: 19163606 DOI: 10.1109/IEMBS.2008.4650103

Skin colour is vital information in dermatological diagnosis. It reflects pathological condition beneath the skin and commonly being used to indicate the extent of a disease. Psoriasis is a skin disease which is indicated by the appearance of red plaques. Although there is no cure for psoriasis, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to determine severity of psoriasis lesion. Erythema (redness) is one parameter in PASI. Commonly, the erythema is assessed visually, thus leading to subjective and inconsistent result. In this work, we proposed an objective assessment of psoriasis erythema for PASI scoring. The colour of psoriasis lesion is analyzed by DeltaL, Deltahue, and Deltachroma of CIELAB colour space. References of lesion with different scores are obtained from the selected lesions by two dermatologists. Results based on 38 lesions from 22 patients with various level of skin pigmentation show that PASI erythema score can be determined objectively and consistent with dermatology scoring.

Matched MeSH terms: Diagnosis, Computer-Assisted
Rosline Hassan: building the future of haemato-pathology in Malaysia

Burki TK

Lancet Haematol, 2021 Aug;8(8):e551.
PMID: 34329575 DOI: 10.1016/S2352-3026(21)00215-5
Matched MeSH terms: Diagnosis, Computer-Assisted
Fulltext Towards precision medicine: from quantitative imaging to radiomics

Acharya UR, Hagiwara Y, Sudarshan VK, Chan WY, Ng KH

J Zhejiang Univ Sci B, 2018 1 9;19(1):6-24.
PMID: 29308604 DOI: 10.1631/jzus.B1700260

Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

Matched MeSH terms: Diagnosis, Computer-Assisted
Fulltext A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

Kamarudin ND, Ooi CY, Kawanabe T, Odaguchi H, Kobayashi F

J Healthc Eng, 2017;2017:7460168.
PMID: 29065640 DOI: 10.1155/2017/7460168

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

Matched MeSH terms: Diagnosis, Computer-Assisted
Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data

Mohktar MS, Redmond SJ, Antoniades NC, Rochford PD, Pretto JJ, Basilakis J, et al.

Artif Intell Med, 2015 Jan;63(1):51-9.
PMID: 25704112 DOI: 10.1016/j.artmed.2014.12.003

BACKGROUND: The use of telehealth technologies to remotely monitor patients suffering chronic diseases may enable preemptive treatment of worsening health conditions before a significant deterioration in the subject's health status occurs, requiring hospital admission.
OBJECTIVE: The objective of this study was to develop and validate a classification algorithm for the early identification of patients, with a background of chronic obstructive pulmonary disease (COPD), who appear to be at high risk of an imminent exacerbation event. The algorithm attempts to predict the patient's condition one day in advance, based on a comparison of their current physiological measurements against the distribution of their measurements over the previous month.
METHOD: The proposed algorithm, which uses a classification and regression tree (CART), has been validated using telehealth measurement data recorded from patients with moderate/severe COPD living at home. The data were collected from February 2007 to January 2008, using a telehealth home monitoring unit.
RESULTS: The CART algorithm can classify home telehealth measurement data into either a 'low risk' or 'high risk' category with 71.8% accuracy, 80.4% specificity and 61.1% sensitivity. The algorithm was able to detect a 'high risk' condition one day prior to patients actually being observed as having a worsening in their COPD condition, as defined by symptom and medication records.
CONCLUSION: The CART analyses have shown that features extracted from three types of physiological measurements; forced expiratory volume in 1s (FEV1), arterial oxygen saturation (SPO2) and weight have the most predictive power in stratifying the patients condition. This CART algorithm for early detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patient's health. This study highlights the potential usefulness of automated analysis of home telehealth data in the early detection of exacerbation events among COPD patients.

Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
Fulltext How can gastro-intestinal tuberculosis diagnosis be improved? A prospective cohort study

Lowbridge C, Fadhil SAM, Krishnan GD, Schimann E, Karuppan RM, Sriram N, et al.

BMC Infect Dis, 2020 Mar 30;20(1):255.
PMID: 32228479 DOI: 10.1186/s12879-020-04983-y

BACKGROUND: Gastrointestinal tuberculosis (TB) is diagnostically challenging; therefore, many cases are treated presumptively. We aimed to describe features and outcomes of gastrointestinal TB, determine whether a clinical algorithm could distinguish TB from non-TB diagnoses, and calculate accuracy of diagnostic tests.
METHODS: We conducted a prospective cohort study of hospitalized patients in Kota Kinabalu, Malaysia, with suspected gastrointestinal TB. We recorded clinical and laboratory characteristics and outcomes. Tissue samples were submitted for histology, microscopy, culture and GeneXpert MTB/RIF®. Patients were followed for up to 2 years.
RESULTS: Among 88 patients with suspected gastrointestinal TB, 69 were included in analyses; 52 (75%) had a final diagnosis of gastrointestinal TB; 17 had a non-TB diagnosis. People with TB were younger (42.7 versus 61.5 years, p = 0.01) and more likely to have weight loss (91% versus 64%, p = 0.03). An algorithm using age 26 g/L, platelets > 340 × 109/L and immunocompromise had good specificity (96.2%) in predicting TB, but very poor sensitivity (16.0%). GeneXpert® performed very well on gastrointestinal biopsies (sensitivity 95.7% versus 35.0% for culture against a gold standard composite case definition of confirmed TB). Most patients (79%) successfully completed treatment and no treatment failure occurred, however adverse events (21%) and mortality (13%) among TB cases were high. We found no evidence that 6 months of treatment was inferior to longer courses.
CONCLUSIONS: The prospective design provides important insights for clinicians managing gastrointestinal TB. We recommend wider implementation of high-performing diagnostic tests such as GeneXpert® on extra-pulmonary samples.

Matched MeSH terms: Diagnosis, Computer-Assisted