We designed this study to evaluate the association of serum ferritin levels with non-scarring alopecia in women. MethodologyAll patients were diagnosed by performing a clinical examination of the crown part width and occiput. Ludwig's classification was used to categorize the cases into grades I-III. Different laboratory tests were performed for the baseline investigation, including serum iron, total iron-binding capacity (TIBC), hemogram, and thyroid function tests. Of the 5 ml of venous blood drawn for routine biochemical tests, 3 ml was stored at -20°C for measuring serum ferritin, while the other 2 ml was sent for a complete blood count. Student's t-test, a chi-square test, and Fisher's exact test were used for comparing the variables. ResultsThis study recruited 100 cases of alopecia. Out of them, 46% of patients were diagnosed with alopecia areata, 25% of cases reported androgenetic alopecia, and 29% of cases of telogen effluvium were also observed. We observed overall mean serum ferritin levels of 20.47±17.50 and 27.87±17.51 in the case versus the control group with a statistically significant difference of 0.005. ConclusionOur study shows that iron stores are one of the independent hazards of alopecia in non-menopausal women. Thus, proper laboratory examination is needed to manage the disease prevalence and prognosis.

Aims: This meta-analysis aims to quantify the effectiveness of artificial intelligence (AI)-supported colonoscopy compared to standard colonoscopy in adenoma detection rate (ADR) differences with the use of computer-aided detection and quality control systems. Moreover, the polyp detection rate (PDR) intergroup differences and withdrawal times will be analyzed. Methods: This study was conducted adhering to PRISMA guidelines. Studies were searched across PubMed, CINAHL, EMBASE, Scopus, Cochrane, and Web of Science. Keywords including the following ‘Artificial Intelligence, Polyp, Adenoma, Detection, Rate, Colonoscopy, Colorectal, Colon, Rectal’ were used. Odds ratio (OR) applying 95% CI for PDR and ADR were computed. SMD with 95% CI for withdrawal times were computed using RevMan 5.4.1 (Cochrane). The risk of bias was assessed using the RoB 2 tool. Results: Of 2562 studies identified, 11 trials were included comprising 6856 participants. Of these, 57.4% participants were in the AI group and 42.6% individuals were in in the standard group. ADR was higher in the AI group compared to the standard of care group (OR=1.51, P=0.003). PDR favored the intervened group compared to the standard group (OR=1.89, P<0.0001). A medium measure of effect was found for withdrawal times (SMD=0.25, P<0.0001), therefore with limited practical applications. Conclusion: AI-supported colonoscopies improve PDR and ADR; however, no noticeable worsening of withdrawal times is noted. Colorectal cancers are highly preventable if diagnosed early-on. With AI-assisted tools in clinical practice, there is a strong potential to reduce the incidence rates of cancers in the near future.