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Quality of life of leprosy patients in Sabah

Gan TS, Voo SYM

Med J Malaysia, 2021 Jan;76(1):56-60.
PMID: 33510110

OBJECTIVE: To determine the Dermatology Life Quality Index (DLQI) among the subtypes of leprosy and to examine correlation with deformity and lepra reactions.
METHODS: This was a cross-sectional study done at Dermatology Outpatient Clinic, Queen Elizabeth Hospital and two health clinics in Kota Kinabalu between 1st April 2019 and 30th November 2019. A standardised case report form was formulated to collect the demographic data and disease profile of the leprosy patients. The quality of life (QoL) was assessed using Dermatology Life Quality Index (DLQI) questionnaire.
RESULTS: A total of 54 patients were included with a male to female ratio of 2.4:1 (38 males and 16 females). The mean DLQI score was 8.31±6.15. The difference between the mean DLQI scores among the leprosy subtypes was not significant. The most affected domain was symptoms and feeling followed by daily activities and leisure. Twenty-one patients (38.9%) had facial deformity and they were found to have significantly higher DLQI score. WHO grade 1 and 2 disability were observed in 37 patients (68.5%) with higher DLQI score compared to those without any disability. More than half of patients with MB leprosy (52.2%) developed lepra reactions but the difference of mean DLQI scores were not significant.
CONCLUSIONS: Leprosy-related disabilities may predispose patients to develop psychosocial problems which may have negative impact on QoL. Thus, periodic assessment of QoL should be incorporated into the management of leprosy patients.
Ocular co-morbidities in patients with atopic dermatitis - a cross-sectional study from a tertiary referral hospital, Malaysia

Gan TS, Juares Rizal A, Salim NL, Lau LL, Voo SYM

Med J Malaysia, 2022 Jan;77(1):6-11.
PMID: 35086988

INTRODUCTION: Atopic dermatitis (AD) is a chronic relapsing pruritic inflammatory skin disease that commonly occurs among children as well as adults. AD patients were reported to have high prevalence of ocular manifestations, which may be due to the disease nature or drug complications. This study aimed to determine the prevalence of ocular manifestations in patients with AD.
MATERIALS AND METHODS: Eighty patients who fulfilled the UK Working Party's Diagnostic Criteria for Atopic Dermatitis were included in the cross-sectional study. A standardized case report form was formulated to collect the demographic data and disease profile of the participants. AD severity was evaluated using the EASI and SCORAD score. All patients underwent a complete ophthalmological evaluation.
RESULTS: The prevalence of ocular manifestations among the patients with AD was 48.8%. Fifty-four (67.5%) patients had facial dermatitis and 37 (46.2%) showed periorbital signs. The mean AD disease duration was 10.99 ± 11.20 years. Majority of the patients had mild to moderate AD. The most frequent ocular manifestation was allergic conjunctivitis (18.75%) followed by cataract (8.75%) and ocular hypertension (8.75%). Among the patients with ocular manifestations, 27 (69.2%) patients regularly applied topical corticosteroids on the face. The use of systemic corticosteroids was seen in 19 (42.2%) patients. Prolonged AD duration was significantly associated with the development of ocular manifestations.
CONCLUSIONS: Nearly half of the patients with AD were complicated with ocular disease regardless of the AD severity, facial dermatitis and presence of periorbital signs. Long disease duration is associated with ocular manifestations, especially steroid related complications.
Fulltext Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Ali S, Ghatwary N, Jha D, Isik-Polat E, Polat G, Yang C, et al.

Sci Rep, 2024 Jan 23;14(1):2032.
PMID: 38263232 DOI: 10.1038/s41598-024-52063-x

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.

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