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.

METHOD: Patient records from a single surgery centre were searched for all patients presenting with late fracture complication following arthroscopically assisted acromioclavicular stabilization. Medical reports including the operative notes and pre- and post-operative X-rays were reviewed. A telephone interview was conducted with each patient to access the American Shoulder and Elbow Surgeons shoulder score.

RESULTS: A total of four patients presented with late fracture complication following arthroscopic-assisted ACJ stabilization surgery. All patients were males and presented following trauma at a median duration of 19.5 months after the index surgery. Fracture morphology differed between patients; the treatment was conservative in three patients, while one patient underwent osteosynthesis.

CONCLUSION: Traumatic peri-implant fractures can occur, even 2 years after arthroscopically assisted ACJ reconstruction. This needs to be considered when planning for surgical intervention in acute ACJ disruption, especially in a high-risk population.

DISCUSSION: This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

METHODS: This study enrolled 147 SLE patients from the Asia Pacific Lupus Collaboration (APLC) cohort, who had BMD and TBS assessed from January 2018 until December 2018. Twenty-eight patients sustaining VF and risk factors associated with increased fracture occurrence were evaluated. Independent risk factors and diagnostic accuracy of VF were analyzed by logistic regression and ROC curve, respectively.

RESULT: The prevalence of vertebral fracture among SLE patients was 19%. BMD, T-score, TBS, and TBS T-score were significantly lower in the vertebral fracture group. TBS exhibited higher positive predictive value and negative predictive value than L spine and left femur BMD for vertebral fractures. Moreover, TBS had a higher diagnostic accuracy than densitometric measurements (area under curve, 0.811 vs. 0.737 and 0.605).