Intrahepatic Gallbladder

Gallbladders have been reported in various ectopic locations.An intrahepatic gallbladder is one where the gallbladder lies within the liver parenchyma or has a subcapsular location along the anterior inferior right lobe of the liver. Intrahepatic gallbladders can have impaired function leading to the formation of gallbladder stones. We present the case of a 57-year-old man who was found to have an intrahepatic gallbladder which was not detected before a retrograde open cholecystectomy was carried out without intra-operative or postoperative complications. Awareness of ectopic gallbladdersand their recognition allows proper intra-operative planning

Temporally Aligning Long Audio Interviews with Questions: A Case Study in Multimodal Data Integration

The problem of audio-to-text alignment has seen significant amount of research using complete supervision during training. However, this is typically not in the context of long audio recordings wherein the text being queried does not appear verbatim within the audio file. This work is a collaboration with a non-governmental organization called CARE India that collects long audio health surveys from young mothers residing in rural parts of Bihar, India. Given a question drawn from a questionnaire that is used to guide these surveys, we aim to locate where the question is asked within a long audio recording. This is of great value to African and Asian organizations that would otherwise have to painstakingly go through long and noisy audio recordings to locate questions (and answers) of interest. Our proposed framework, INDENT, uses a cross-attention-based model and prior information on the temporal ordering of sentences to learn speech embeddings that capture the semantics of the underlying spoken text. These learnt embeddings are used to retrieve the corresponding audio segment based on text queries at inference time. We empirically demonstrate the significant effectiveness (improvement in R-avg of about 3%) of our model over those obtained using text-based heuristics. We also show how noisy ASR, generated using state-of-the-art ASR models for Indian languages, yields better results when used in place of speech. INDENT, trained only on Hindi data is able to cater to all languages supported by the (semantically) shared text space. We illustrate this empirically on 11 Indic languages.Comment: Work Accepted in IJCAI-23- AI and Social Good Trac

Anterior cruciate ligament repair – past, present and future

Abstract Background This article provides a detailed narrative review on the history and current concepts surrounding ligamentous repair techniques in athletic patients. In particular, we will focus on the anterior cruciate ligament (ACL) as a case study in ligament injury and ligamentous repair techniques. PubMed (MEDLINE), EMBASE and Cochrane Library databases for papers relating to primary anterior cruciate ligament reconstruction were searched by all participating authors. All relevant historical papers were included for analysis. Additional searches of the same databases were made for papers relating to biological enhancement of ligament healing. Current standard The poor capacity of the ACL to heal is one of the main reasons why the current gold standard surgical treatment for an ACL injury in an athletic patient is ACL reconstruction with autograft from either the hamstrings or patella tendon. It is hypothesised that by preserving and repairing native tissues and negating the need for autograft that primary ACL repair may represent a key step change in the treatment of ACL injuries. History of primary ACL repair The history of primary ACL repair will be discussed and the circumstances that led to the near-abandonment of primary ACL repair techniques will be reviewed. New primary repair techniques There has been a recent resurgence in interest with regards to primary ACL repair. Improvements in imaging now allow for identification of tear location, with femoral-sided injuries, being more suitable for repair. We will discuss in details strategies for improving the mechanical and biological environment in order to allow primary healing to occur. In particular, we will explain mechanical supplementation such as Internal Brace Ligament Augmentation and Dynamic Intraligamentary Stabilisation techniques. These are novel techniques that aim to protect the primary repair by providing a stabilising construct that connects the femur and the tibia, thus bridging the repair. Bio enhanced repair In addition, biological supplementation is being investigated as an adjunct and we will review the current literature with regards to bio-enhancement in the form platelet rich plasma, bio-scaffolds and stem cells. On the basis of current evidence, there appears to be a role for bio-enhancement, however, this is not yet translated into clinical practice. Conclusions Several promising avenues of further research now exist in the form of mechanical and biological augmentation techniques. Further work is clearly needed but there is renewed interest and focus for primary ACL repair that may yet prove the new frontier in ligament repair

Artificial Intelligence in Healthcare: 2023 Year in Review Dataset

Background:The infodemic we are experiencing with AI related publications in healthcare is unparalleled. The excitement and fear surrounding the adoption of rapidly evolving AI in healthcare applications pose a real challenge. Collaborative learning from published research is one of the best ways to understand the associated opportunities and challenges in the field. To gain a deep understanding of recent developments in this field, we have conducted a quantitative and qualitative review of AI in healthcare research articles published in 2023.Methods:We performed a PubMed search using the terms, “machine learning” or “artificial intelligence” and “2023”, restricted to English language and human subject research as of December 31, 2023 on January 1, 2024. Utilizing a Deep Learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics. Similarly, we performed a search for Large Language Model(LLM) based publications for the year 2023.Results:Our PubMed search yielded 23,306 articles, of which 1,612 were classified as mature. Following exclusions, 1,226 articles were selected for final analysis. Among these, the highest number of articles originated from the Imaging specialty (483), followed by Gastroenterology (86), and Ophthalmology (78). Analysis of data types revealed that image data was predominant, utilized in 75.2% of publications, followed by tabular data (12.9%) and text data (11.6%). Deep Learning models were extensively employed, constituting 59.8% of the models used. For the LLM related publications,after exclusions, 584 publications were finally classified into the 26 different healthcare specialties and used for further analysis. The utilization of Large Language Models (LLMs), is highest in general healthcare specialties, at 20.1%, followed by surgery at 8.5%.Conclusion:Image based healthcare specialities such as Radiology, Gastroenterology and Cardiology have dominated the landscape of AI in healthcare research for years. In the future, we are likely to see other healthcare specialties including the education and administrative areas of healthcare be driven by the LLMs and possibly multimodal models in the next era of AI in healthcare research and publications.Data Files Description:Here, we are providing two data files. The first file, named FinalData_2023_YIR, contains 1267 rows with columns including 'DOI', 'Title', 'Abstract', 'Author Name', 'Author Address', 'Specialty', 'Data type', 'Model type', and 'Systematic Reviews'. The columns 'Specialty', 'Data type', 'Model type', and 'Systematic Reviews' were manually annotated by the BrainX AI research team. The second file, named Final_LLM_2023_YIR, consists of 584 rows and columns including 'DOI', 'Title', 'Abstract', 'Author Name', 'Author Address', 'Journal', and 'Specialty'. Here, the 'Specialty' column was also manually annotated by the BrainX AI Research Team.</p

Efficacy, Safety and Immunogenicity of Sun’s Ranibizumab Biosimilar in Neovascular Age-Related Macular Degeneration: A Phase 3, Double-Blind Comparative Study

Abstract Introduction The study aimed to evaluate comparability in terms of efficacy, safety and immunogenicity of Sun’s ranibizumab biosimilar with reference ranibizumab in patients with neovascular age-related macular degeneration (nAMD). Methods This prospective, randomised, double-blind, two-group, parallel-arm, multicentre, phase 3 comparative study included patients with nAMD ≥ 50 years, randomised (in a 2:1 ratio) in a double-blind manner to receive 0.5 mg (0.05 mL) intravitreal injection of either Sun’s ranibizumab or reference ranibizumab in the study eye every 4 weeks until week 16 (total of four doses). Results Primary endpoint results demonstrated equivalence in the proportion of patients who lost fewer than 15 letters from baseline best-corrected visual acuity (BCVA) to the end of week 16 (99% of patients in Sun’s ranibizumab and 100% in reference ranibizumab; p > 0.9999), with the proportional difference (90% confidence interval) at −1% (−2.51, +0.61) lying within a pre-specified equivalence margin. Visual acuity improved by 15 or more letters in 43% of Sun’s ranibizumab group and 37% of the reference ranibizumab group (p = 0.4267). The mean increase in BCVA was 15.7 letters in Sun’s ranibizumab group and 14.6 letters in the reference ranibizumab group (p < 0.001 within both groups and p = 0.5275 between groups). The mean change in central macular thickness was comparable between groups (p = 0.7946). Anti-ranibizumab antibodies were found in one patient of the reference ranibizumab group, while neutralising antibodies were not found in any patients. Both products were well tolerated. Conclusion Sun’s ranibizumab biosimilar is found to be therapeutically equivalent to reference ranibizumab in patients with nAMD. There were no additional safety or immunogenicity concerns. Trial Registration CTRI/2020/09/027629, registered on 07 September 2020

Assessment of clinical information quality in digital health technologies: an international eDelphi study

BACKGROUND: Digital health technologies (DHTs), such as electronic health records and prescribing systems, are transforming health care delivery around the world. The quality of information in DHTs is key to the quality and safety of care. We developed a novel clinical information quality (CLIQ) framework to assess the quality of clinical information in DHTs. OBJECTIVE: This study explored clinicians' perspectives on the relevance, definition, and assessment of information quality dimensions in the CLIQ framework. METHODS: We used a systematic and iterative eDelphi approach to engage clinicians who had information governance roles or personal interest in information governance; the clinicians were recruited through purposive and snowball sampling techniques. Data were collected using semistructured online questionnaires until consensus was reached on the information quality dimensions in the CLIQ framework. Responses on the relevance of the dimensions were summarized to inform decisions on retention of the dimensions according to prespecified rules. Thematic analysis of the free-text responses was used to revise definitions and the assessment of dimensions. RESULTS: Thirty-five clinicians from 10 countries participated in the study, which was concluded after the second round. Consensus was reached on all dimensions and categories in the CLIQ framework: informativeness (accuracy, completeness, interpretability, plausibility, provenance, and relevance), availability (accessibility, portability, security, and timeliness), and usability (conformance, consistency, and maintainability). A new dimension, searchability, was introduced in the availability category to account for the ease of finding needed information in the DHTs. Certain dimensions were renamed, and some definitions were rephrased to improve clarity. CONCLUSIONS: The CLIQ framework reached a high expert consensus and clarity of language relating to the information quality dimensions. The framework can be used by health care managers and institutions as a pragmatic tool for identifying and forestalling information quality problems that could compromise patient safety and quality of care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-057430