Dentistry based on artificial intelligence (AI) is not a myth but turning into a reality. AI has revolutionized medicine and dentistry in various ways. AI is a technology that uses machines to imitate intelligent human behavior. AI is gaining popularity worldwide because of its significant impact and breakthrough in the field of intelligence innovation. It is a lifesaver in dentistry, particularly in the field of prosthodontics, because it aids in the design of prostheses and the fabrication of functional maxillofacial appliances. It also helps in the processes of patient documentation, diagnosis, treatment planning, and patient management, allowing oral healthcare professionals to work smarter rather than harder. While it cannot replace the work of a dentist because dentistry is not about disease diagnosis, it does involve correlation with other clinical findings and provides treatment to the patient. The integration of AI and digitization has brought a new paradigm in dentistry, with extremely promising prospects. The availability of insufficient and inaccurate data is now the only barrier to the deployment of AI. Therefore, dentists and clinicians must focus on collecting and entering authentic data into their database, which will be completely utilized for AI in dentistry shortly. This study focuses on various applications of AI in prosthodontics along with its limitations and future scope.
The term "temporomandibular disorders" (TMDs) refers to a variety of problems involving the muscles of the masticatory system and the jaw. The most common symptoms of TMD are pain in the face, headaches, clicking or popping in the joints, and difficulties with jaw function. The severity of TMD can be measured with a number of different scales, including the Helkimo, Craniomandibular Index (CMI), Mandibular Functional Impairment Questionnaire (MFIQ), Fonseca scale and Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) scales. The former focuses on the patient's chief complaint, while the latter takes into account secondary symptoms such as limited mobility, impaired temporomandibular joint (TMJ) function, muscle pain, and discomfort during mandibular motion. According to the severity of the issue, the results can be used to categorise the situation. To effectively treat TMD, one must first determine their index score and then formulate a treatment strategy based on that score.
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The increased memory demands of workloads is putting high pressure on Last Level Caches (LLCs). Unfortunately, there is limited opportunity to increase the capacity of LLCs due to the area and power requirements of the underlying SRAM technology. Interestingly, emerging Non-Volatile Memory (NVM) technologies promise a feasible alternative to SRAM for LLCs due to their higher area density. However, NVMs have substantially higher read and write latencies, which offset their area density benefit. Although researchers have proposed methods to tolerate NVM's increased write latency, little emphasis has been placed on reducing the critical NVM read latency.To address this problem, this paper proposes Cloak. Cloak exploits data reuse in the LLC at the page level, to hide NVM read latency. Specifically, on certain L1 TLB misses to a page, Cloak transfers LLC-resident data belonging to the page from the LLC NVM array to a set of small SRAM Page Buffers that will service subsequent requests to this page. Further, to enable the high-bandwidth, low-latency transfer of lines of a page to the page buffers, Cloak uses an LLC layout that accelerates the discovery of LLC-resident cache lines from the page. We evaluate Cloak with full-system simulations of a 4-core processor across 14 workloads. We find that, on average, Cloak outperforms an SRAM LLC by 23.8% and an NVM-only LLC by 8.9%-in both cases, with negligible additional area. Further, Cloak's ED 2 is 39.9% and 17.5% lower, respectively, than these designs.
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