METHODS: Data for this study were obtained from final year medical students' exit examination (n=185). Retrospective analysis of data was conducted using SPSS. Means for the six CSs assessed across the 16 stations were computed and compared.
RESULTS: Means for history taking, physical examination, communication skills, clinical reasoning skills (CRSs), procedural skills (PSs), and professionalism were 6.25±1.29, 6.39±1.36, 6.34±0.98, 5.86±0.99, 6.59±1.08, and 6.28±1.02, respectively. Repeated measures ANOVA showed there was a significant difference in the means of the six CSs assessed [F(2.980, 548.332)=20.253, p<0.001]. Pairwise multiple comparisons revealed significant differences between the means of the eight pairs of CSs assessed, at p<0.05.
CONCLUSIONS: CRSs appeared to be the weakest while PSs were the strongest, among the six CSs assessed. Students' unsatisfactory performance in CRS needs to be addressed as CRS is one of the core competencies in medical education and a critical skill to be acquired by medical students before entering the workplace. Despite its challenges, students must learn the skills of clinical reasoning, while clinical teachers should facilitate the clinical reasoning process and guide students' clinical reasoning development.
METHODS: The video was developed using the BC delay explanatory model. A self-administered pre- and post-survey on 241 newly diagnosed BC patients in University Malaya Medical Center was performed. The Wilcoxon matched paired signed rank test was used to evaluate patients' pre and post perceived knowledge using a Likert scale 0 to 4 (0 = "no knowledge," 4 = "a great degree of knowledge"). Treatment adherence among participants were measured after 1-year follow-up.
RESULTS: Eighty percent of the patients reported that the video met or exceeded their expectations. In total 80.5% reported that the video was very effective and effective in improving their perspective on BC treatments. There was improvement in perceived knowledge for treatment options (mean scores; M = 0.93 versus M = 2.97) (p < 0.001) and also for perceived knowledge on types of operation, information on chemotherapy, radiotherapy, hormone therapy, healthy diet, physical activity after treatments, and care of the arm after operation(p < 0.001). In total 89.4%, 79.3%, and 85.9% adhered to surgical, chemotherapy, and radiotherapy recommended treatment, respectively.
CONCLUSION: The video improved patients' perceived knowledge and satisfaction. The program improved access not only to new BC patients but also the public and found sustainable using the YouTube platform.
FINDINGS: Our high-throughput workflow minimizes these risks via a 4-step strategy: (i) technical replication with 2 PCR replicates and 2 extraction replicates; (ii) using multi-markers (12S,16S,CytB); (iii) a "twin-tagging," 2-step PCR protocol; and (iv) use of the probabilistic taxonomic assignment method PROTAX, which can account for incomplete reference databases. Because annotation errors in the reference sequences can result in taxonomic misassignment, we supply a protocol for curating sequence datasets. For some taxonomic groups and some markers, curation resulted in >50% of sequences being deleted from public reference databases, owing to (i) limited overlap between our target amplicon and reference sequences, (ii) mislabelling of reference sequences, and (iii) redundancy. Finally, we provide a bioinformatic pipeline to process amplicons and conduct PROTAX assignment and tested it on an invertebrate-derived DNA dataset from 1,532 leeches from Sabah, Malaysia. Twin-tagging allowed us to detect and exclude sequences with non-matching tags. The smallest DNA fragment (16S) amplified most frequently for all samples but was less powerful for discriminating at species rank. Using a stringent and lax acceptance criterion we found 162 (stringent) and 190 (lax) vertebrate detections of 95 (stringent) and 109 (lax) leech samples.
CONCLUSIONS: Our metabarcoding workflow should help research groups increase the robustness of their results and therefore facilitate wider use of environmental and invertebrate-derived DNA, which is turning into a valuable source of ecological and conservation information on tetrapods.
METHODS: From a prospective database of endoscopic procedures maintained by the senior author, clinical data, imaging studies, operative charts, and videos of cases of forehead osteomas were retrieved and analyzed. The pertinent literature was also reviewed.
RESULTS: The surgical technique of the fully endoscopic resection of frontal osteomas was formulated.
CONCLUSION: The endoscopic technique has many advantages over the conventional procedures. In our hands, the technique has proven to be less time-consuming, efficient, and minimally invasive with excellent cosmetic results.
MATERIALS AND METHODS: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
RESULTS: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
CONCLUSION: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.
MATERIALS AND METHODS: All patients who had 3D-ABUS between January 2014 and January 2022 for screening were included in this retrospective study. The images were reported by 1 of 6 breast radiologists based on the Breast Imaging Reporting and Data Systems (BI-RADS). The 3D-ABUS was reviewed together with the digital breast tomosynthesis (DBT). Recall rate, biopsy rate, positive predictive value (PPV) and cancer detection yield were calculated.
RESULTS: In total, 3616 studies were performed in 1555 women (breast density C/D 95.5% (n = 3455/3616), breast density A/B 4.0% (n = 144/3616), density unknown (0.5% (n = 17/3616)). A total of 259 lesions were detected on 3D-ABUS (87.6% (n = 227/259) masses and 12.4% (n = 32/259) architectural distortions). The recall rate was 5.2% (n = 188/3616) (CI 4.5-6.0%) with only 36.7% (n = 69/188) cases recalled to another date. Moreover, recall declined over time. There were 3.4% (n = 123/3616) biopsies performed, with 52.8% (n = 65/123) biopsies due to an abnormality detected in 3D-ABUS alone. Ten of 65 lesions were malignant, resulting in a positive predictive value (PPV) of 15.4% (n = 10/65) (CI 7.6-26.5%)). The cancer detection yield of 3D-ABUS is 2.77 per 1000 screening tests (CI 1.30-5.1).
CONCLUSION: The cancer detection yield of 3D-ABUS in a real clinical screening setting is comparable to the results reported in previous prospective studies, with lower recall and biopsy rates. 3D-ABUS also may be an alternative for screening when mammography is not possible or declined.
CLINICAL RELEVANCE STATEMENT: 3D automated breast ultrasound screening performance in a clinical setting is comparable to previous prospective studies, with better recall and biopsy rates.
KEY POINTS: • 3D automated breast ultrasound is a reliable and reproducible tool that provides a three-dimensional representation of the breast and allows image visualisation in axial, coronal and sagittal. • The diagnostic performance of 3D automated breast ultrasound in a real clinical setting is comparable to its performance in previously published prospective studies, with improved recall and biopsy rates. • 3D automated breast ultrasound is a useful adjunct to mammography in dense breasts and may be an alternative for screening when mammography is not possible or declined.