Rituximab in Childhood and Juvenile Pemphigus Vulgaris: A Systematic Review.

Pemphigus vulgaris (PV) is a chronic autoimmune blistering disorder characterized by the loss of intraepithelial adhesion, affecting the skin and mucous membranes. Both males and females are affected, although it predominantly affects females in their fifth and sixth decades of life. Approximately 1.4 to 3.7% of PV cases occur in the pediatric population (≤18 years of age), and may be classified into childhood/pediatric PV, which affects individuals under 12 years old, and juvenile/adolescent PV, affecting those between 12 and 18 years old. Due to its rare occurrence in children and adolescents, there is often a delay in diagnosis and treatment in this age group. A systematic literature search was conducted on MEDLINE/PubMed, Web of Science, EMBASE, SCOPUS, and Cochrane Library databases to evaluate the efficacy of rituximab (RTX) in childhood and juvenile PV patients. The Joanna Briggs Institute (JBI) Critical Appraisal Checklist was employed to assess the risk of bias in case reports and series, while the Cochrane ROBINS-I tool was utilized for evaluating observational studies or non-randomized intervention studies. A total of 18 studies encompassing 46 juvenile or childhood PV patients in the pediatric and adolescent age groups were included for qualitative synthesis. The studies included nine case reports, two case series, five retrospective studies, one prospective study, and one open-label pilot study. Almost all cases of childhood and juvenile PV achieved either complete or partial remission after undergoing RTX treatment during the final follow-up periods. Furthermore, most cases reported no relapse, and only minor adverse events were noted in the RTX treatment group. Despite its potential benefits, the utilization of RTX in pediatric patients raises concerns due to the scarcity of evidence and the absence of controlled studies specific to this age group. Further exploration is necessary to establish a standardized treatment regimen for RTX in pediatric PV, which involves identifying the optimal dosage, frequency, treatment cycle duration, and maintenance therapy duration.

Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review.

BACKGROUND: Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS: A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS: Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION: Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.

Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis.

BACKGROUND: The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors. METHODS: A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT. RESULTS: 16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier. CONCLUSION: The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.

Diagnostic accuracy of Vibrational spectroscopy in the diagnosis of oral potentially malignant and malignant disorders: A systematic review and meta-analysis.

Oral malignant and potentially malignant conditions affect several people worldwide each year. The early diagnoses of these conditions play an important role in prevention and recovery. Vibrational spectroscopy techniques such as Raman spectroscopy (RS) and Fourier-transform infrared (FTIR) spectroscopy are used in the early, non-invasive, label-free diagnosis of malignant and pre-malignant conditions, and are areas of active research. However, there is no conclusive evidence suggesting the translatability of these methods into clinical practice. This systematic review and meta-analysis presents pooled evidence for RS and FTIR methods in the detection of malignant and potentially malignant conditions of the oral cavity. Electronic databases were searched for published literature on RS and FTIR in the diagnosis of oral malignant and potentially malignant conditions. The pooled sensitivity, specificity, diagnostic accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), pre-test, and post-test probability were then calculated using the random-effects model. A subgroup analysis was conducted separately for RS and FTIR methods. A total of 12 studies were included (8 of RS; 4 of FTIR) as per the eligibility criteria. The pooled sensitivity and specificity of the vibrational spectroscopy methods were calculated to be 0.99 (95% confidence interval [CI]: 0.90, 1.00) and 0.94 (95% CI: 0.85, 0.98), respectively. The area under the curve (AUC) for the summary receiving operator characteristic curve was found to be 0.99 (0.98-1.00). Therefore, the results obtained in this study suggest that the RS and FTIR methods offer great potential to be used in the early diagnosis of oral malignant and pre-malignant conditions.

Comparative effectiveness of medicinal interventions for oral submucous fibrosis: A network meta-analysis.

OBJECTIVES: This network meta-analysis presents an exhaustive description and comparison of the available medical interventions for the management of oral submucous fibrosis (OSMF). MATERIALS AND METHODS: A systematic review and network meta-analysis was conducted after registration with PROSPERO. (PROSPERO ID CRD42022303441). Databases (PubMed, Cochrane, EMBASE, Web of Science, and others) were searched for randomized clinical trials (RCT) trials from inception till September 2022 for the medical interventions in OSMF. The primary outcome was the improvement in mouth opening. The secondary outcomes were improvement in burning sensation, tongue protrusion, and cheek flexibility. The interventions were ranked according to their efficacy based on the surface under the cumulative ranking. RESULTS: 47 studies including 2393 patients were assessed for quantitative analysis. For mouth opening, the combined treatment with steroid, hyaluronidase, and antioxidant was most effective [MD, 7.05 (95%CI 1.76,12.34)], followed by the combination of oral antioxidants with injectable steroids, [MD, 3.80 (95%CI -0.44,8.03)]. Additionally, the combined treatment with steroid, hyaluronidase, and antioxidant was most effective in reducing the burning sensation [MD, -8.62(-10.95,-6.30)], followed by aloe vera [MD, -8.45(-10.40,-6.49)] and pentoxifylline [MD -7.57(-9.46,-5.68)]. For tongue protrusion, curcumin was most effective followed by antioxidants. Most of the drugs used were reported to cause negligible or mild adverse effects. CONCLUSION: This network meta-analysis reported the efficacy of medicinal interventions in OSMF patients compared to the placebo in the improvement of mouth opening and burning sensation, and cheek flexibility. The methodological quality of included RCTs was low. Well-designed studies are recommended to obtain strong evidence.

Tetracycline impregnated bone grafts in the management of peri-implantitis and guided bone regeneration around dental implants: A systematic review.

Background: Rehabilitation of dental arches with the help of dental implants has been revolutionary and a significant part of research is devoted to increasing its success rate. One of the most common causes of failure of dental implants is peri-implantitis caused due to microbial invasion. Newer strategies are being adapted for the treatment of peri-implantits and recent surgical management with the help of antibiotic-impregnated bone grafts shows a promising future. Aim and objectives: This study aimed to test the efficacy of bone grafts incorporating tetracycline and its derivatives in the treatment of peri-implantits and guided bone regeneration with the estimation of clinical and radiographic parameters. Methods: A thorough search was made on eminent databases such as PubMed, Embase, Scopus, and Cochrane Library database for published literature on tetracycline impregnated bone grafts used either in the management of peri-implantitis or for guided bone regeneration around dental implants.The measures of outcome were clinical attachment loss or probing depth around dental implants and radiographic bone height. Results: Nine potentially eligible full-text published articles including case reports, case series, observational studies, and randomized controlled trials were selected for review. Most of the studies reviewed; reported a reduction in probing depth and an increase in bone height and density after placement of tetracycline bone grafts around the dental implant. Conclusion: The incorporation of tetracycline into the bone grafts shows promising results as an agent of local delivery around dental implants in the management of peri-implantitis and for guided bone regeneration. Future trials are required to produce a body of evidence and to facilitate the translation of this procedure into clinical practice.

Innovative App (ExoDont) and Other Conventional Methods to Improve Patient Compliance After Minor Oral Surgical Procedures: Pilot, Nonrandomized, and Prospective Comparative Study.

BACKGROUND: Postoperative care is influenced by various factors such as compliance, comprehension, retention of instructions, and other unaccounted elements. It is imperative that patients adhere to the instructions and prescribed regimen for smooth and placid healing. ExoDont, an Android-based mobile health app, was designed to ensure a smooth postoperative period for patients after a dental extraction. Besides providing postoperative instructions at defined intervals, the app also sends drug reminders as an added advantage over other available, conventional methods. OBJECTIVE: The aim of this study was to compare the compliance rate of individuals with respect to the prescribed regimen and postoperative instructions. Additionally, we aimed to assess any changes in the postoperative complication rate of patients assigned to 3 categories: the verbal, verbal plus written, and ExoDont app-based delivery groups. METHODS: We conducted a pilot, nonrandomized, and prospective comparative study in which patients after tooth extraction were assigned to 3 groups-verbal (Group A), verbal plus written (Group B), and ExoDont app-based delivery (Group C)-based on the eligibility criteria, and a 1-week follow-up was planned to obtain the responses regarding compliance and postoperative complications from the participants. RESULTS: In total, 90 patients were recruited and equally divided into 3 groups. Compliance to prescribed drug was found to be the highest in Group C, where of the 30 participants, 25 (83%) and 28 (93%) followed the entire course of antibiotics and analgesics, respectively. For postoperative instructions, higher compliance was observed in Group C in relation to compliance to diet restrictions (P=.001), not rinsing for 24 hours (P<.001), and warm saline rinses after 24 hours (P=.001). However, the difference was not significant for smoking restrictions (P=.07) and avoiding alcohol (P=.16). Moreover, the difference in postoperative complication rate was not statistically significant among the 3 groups (P=.31). CONCLUSIONS: As evident from the results, it is anticipated that the ExoDont app will be helpful in circumventing the unaccounted possibilities of missing the prescribed dosage and postoperative instructions and ensuring the smooth recovery of patients after dental extraction. However, future studies are required to establish this app-based method of delivery of postoperative instructions as a viable option in routine clinical practice.

An Innovative App (ExoDont) for Postoperative Care of Patients After Tooth Extraction: Prototype Development and Testing Study.

BACKGROUND: The postoperative period is crucial for the initiation of healing and prevention of complications after any surgical procedure. Due to factors such as poor compliance, comprehension, and retention of instructions, and other unaccounted factors, the objectives of postoperative care are not always achieved. Therefore, an Android-based mobile health app (ExoDont) was developed to ensure a smooth postoperative period for patients after a dental extraction. The ExoDont app delivers reminders for postoperative instructions and drug intake at defined intervals, thus fostering self-reliance among patients in taking their prescribed dose of medication. OBJECTIVE: The aim of this study is to design, develop, and validate ExoDont, an innovative app for improved adherence to postoperative instructions after tooth extraction. METHODS: A postoperative treatment protocol was developed by a team of oral and maxillofacial surgeons and general dentists, following which the clinical and technological requirements of the app were determined along with the software engineers, graphic designers, and applications architect in the team. ExoDont was developed to provide timely reminders for medication and postoperative care. The app was field tested and validated using the User Version of the Mobile Application Rating Scale. RESULTS: The ExoDont software design was divided into a 3-level architecture comprising a user interface application, logical layer, and database layer. The software architecture consists of an Android-based ExoDont app for patients and a web version of the admin panel. The testing and validation of the ExoDont app revealed that Perceived Impact received the highest mean score of all rated components (mean 4.6, SD 0.5), while Engagement received the lowest mean score (mean 3.5, SD 0.8). CONCLUSIONS: The testing and validation of the app support its usability and functionality, as well as its impact on users. The ExoDont app has been designed, keeping the welfare of patients in view, in a user-friendly manner that will help patients adhere to the prescribed drug regimen and ensure easy and efficient dissemination of postoperative instructions. It could play an instrumental role in fostering compliance among patients and significantly decrease the complication rate following dental extractions.