This double-blind positive-controlled study investigated the potential for the aroma of a novel blend of essential oils, Genius to enhance cognitive performance and mood in healthy adults, and whether any such benefits might be related to changes in cerebrovascular oxygenation measured using Near Infra-Red Spectroscopy. Ninety participants (61 female) were pseudo-randomly allocated to achieve a gender balance across three experimental groups: Genius aroma, Sage aroma (positive control) or no aroma (control). All participants completed mood questionnaires after completing a range of cognitive tasks whilst wearing a Near Infra-Red Spectroscopy headband. Multivariate and subsequent univariate data analysis revealed significant enhancements to memory and executive function tasks in the Genius and sage aroma conditions compared to no aroma with larger effects noted for the Genius blend. Furthermore, the novel blend outperformed the aroma of pure sage and also left participants feeling significantly more alert and less fatigued at the end of the testing session. Near Infra-Red Spectroscopy data indicated that both sage and Genius blend enhanced metabolism during task performance with a greater impact from the Genius aroma. Although suggestive of a mechanism underpinning the enhancements observed no correlations were found between the Near Infra-Red Spectroscopy signals and cognitive performance. This study strengthens the evidence base for the beneficial effects of essential oil aroma inhalation for cognitive performance, however the underlying mechanisms remain elusive.
Our descriptive study focused on morphologic characteristics of hyperchromatic crowded groups (HCGs) in ThinPrep cervical cytology tests when reviewed with the artificial intelligence (AI)-assisted Hologic Genius Digital Diagnostics System (HGDDS). After IRB approval, our archives were searched over a 1-year period for potential HCGs. A total of 157 slides with HCGs were selected, scanned, and analyzed using the HGDDS. One cytologist and one cytopathologist interpreted these cases while enumerating the cytomorphologic characteristics as seen with the HGDDS. Of the 157 cases, a total of 84.7% were called Negative for Intraepithelial Lesion or Malignancy (NILM) on original ThinPrep interpretation (OTPI) as opposed to 76.4% with HGDDS. 5.7% and 3.2% of the cases were called high-grade squamous intraepithelial lesion (HSIL) and atypical glandular cells (AGC) on OTPI, as compared to 4.5% and 8.3% on HGDDS. 6.4% of cases were interpreted as adenocarcinoma on both OTPI and HGDDS. A total of 16 cases were called NILM-Atrophy by both modalities. Concordance for pathologist diagnosis between HGDDS and OTPI for 157 cases was 0.610 (kappa value). For 25 cases, there was a follow-up biopsy diagnosis, including 10 cases of adenocarcinoma, 5 of Cervical Intraepithelial Neoplasia (CIN) 2-3, and 1 case of CIN 1. The sensitivity, specificity, positive predictive value, and negative predictive value for the detection of CIN2+ lesions, when ASC-H/AGC and above were considered, were 100%, 50%, 75%, and 100%, respectively. Our initial study shows encouraging results in the evaluation of HCGs presented as two-dimensional static images on a computer monitor by the HGDDS.
Colorectal cancer is a major global health burden, with most cases arising from adenomatous polyps. Although colonoscopy is the gold standard for detection, its effectiveness is operator-dependent. Artificial intelligence-assisted systems have been developed to improve adenoma detection, but their comparative performance remains unclear. We performed a systematic review and Bayesian network meta-analysis of randomized controlled trials comparing artificial intelligence-assisted with standard colonoscopy. PubMed, Scopus, and Google Scholar were searched up to 4 November 2025. Eligible studies included adults undergoing colonoscopy and reporting adenoma detection rate (ADR) and adenomas per colonoscopy (APC). Secondary outcomes included withdrawal time and detection of advanced and sessile serrated lesions. Risk of bias was assessed using Cochrane RoB 2.0, and certainty of evidence was evaluated with CINeMA. A total of 48 randomized controlled trials (34 106 participants) were included. Artificial intelligence-assisted colonoscopy significantly improved ADR compared with standard colonoscopy. EndoAngel showed the greatest effect [odds ratio (OR): 1.84, surface under the cumulative ranking curve (SUCRA): 0.9], followed by EndoAID (OR: 1.64, SUCRA: 0.7), CAD-EYE (OR: 1.46, SUCRA: 0.5), and GI Genius (OR: 1.45, SUCRA: 0.5). For APC, EndoAID demonstrated the largest benefit (mean difference: 0.62). EndoAngel modestly increased withdrawal time (mean difference: 1.14 minutes). No system significantly improved detection of advanced or sessile serrated lesions. Heterogeneity was low, and certainty of evidence was moderate. Artificial intelligence-assisted colonoscopy improves adenoma detection; however, differences between systems are small, and benefits for high-risk lesions remain uncertain. Further head-to-head trials and cost-effectiveness studies are needed.
The aim of this study was to document technical errors encountered during validation of the Genius Digital Diagnostics System (GDDS). A total of 909 cases of archived ThinPrep Pap slides with follow-up biopsies were retrieved. Slides were cleaned, relabeled, and scanned with GDDS. Digital imager errors, including slide events and imager errors, were documented and evaluated. Of the 909 slides scanned, 21 (2.3 %) demonstrated slide events. For 5 cases, the slides had cell focus errors, 12 failed due to quality control (QC) errors, 2 had barcode issues, 1 showed an oversaturated frame, and 1 presented a problem because it was a duplicate. Some errors could be corrected, of which 8 cases with various diagnostic cytology interpretations were successfully rescanned. There were 13 (1.4%) cases that could not be scanned and thus were excluded from the study, predominantly because of focus QC errors due to scratched coverslips from long-term storage. There were 43 imager errors including failure of motor movement, cancellation of slide handling action, and failure to pick slides from the carrier station for which the scanning process had to be paused. Imager errors were solved by rebooting the system, correcting the positioning of the slide on the system, and technical help provided by the vendor. Minor errors are to be expected when digitizing large volume of Pap slides. Total number of rescanned cases to address such technical problems were low in number and did not compromise the interpretation of Pap test slides using GDDS.
The integration of digital whole slide imaging and artificial intelligence is poised to transform cytopathology practice. Our aim was to validate the Hologic Genius Digital Diagnostics System (GDDS) for clinical use in Papanicolaou (Pap) test interpretation by comparing digital interpretations using the GDDS to computer-assisted interpretations using the ThinPrep Imaging System (TIS). The study set consisted of 1748 Pap tests, including 53 unsatisfactory for evaluation cases, 1450 negative for intraepithelial lesion or malignancy (NILM) cases, and 245 atypical squamous cells of undetermined significance or worse cases. Each Pap test was reviewed either retrospectively or prospectively by one of 29 cytologists, and results of review on the GDDS were compared to review on the TIS for concordance. Cases requiring hierarchical review were reviewed by one of 23 pathologists. Concordance was calculated between each method. Diagnostic concordance of 95% was required for validation. The GDDS interpretation was concordant with the TIS interpretation in 1722/1748 cases (98.5%). Of the 26 discordant cases, most (20) were discordances between unsatisfactory and NILM, while 5 were attributed to interpretation error, and one was a low-cellularity low-grade squamous intraepithelial lesion containing rare diagnostic cells in the GDDS tiles that was originally interpreted as NILM on the TIS. Pap test interpretation using the GDDS showed high concordance with interpretations using the TIS, achieving the 95% threshold for clinical validation. Discordances were mainly attributable to the ability of the GDDS to quantify cellularity in low/borderline cellularity cases, or rarely to identify significant abnormalities missed by the TIS.
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The death of chess Grandmaster and content creator Daniel Naroditsky sparked heated debate on the impact of cyberbullying on his mental health in the last 2 years of his life. Cyberbullying remains a widespread public health problem, with strong associations to mental disorders and significant relevance to psychiatric practice worldwide.
The Hologic Genius Digital Diagnostics System (HGDDS) analyzes ThinPrep Papanicolaou (Pap) tests (TPPTs) to assist in detecting cervical lesions. The aim of this study was to determine the sensitivity of the HGDDS in identifying commonly diagnosed microorganisms in Pap tests. A total of 305 TPPT cases were selected from Magee Women's Hospital, University of Pittsburgh, consisting of 244 cases with microorganism diagnoses (a total of 262 cases of Actinomyces, Candida spp, herpes simplex virus [HSV], and Trichomonas) and 61 cases without microorganisms. Slides were scanned and then subjected to artificial intelligence (AI) analysis using the HGDDS and subsequently reviewed on a digital workstation by a cytologist, followed by a resident and a cytopathologist who made the final diagnoses. Diagnosis using the HGDDS demonstrated high sensitivity across all microorganisms (95.4%). Herpes simplex virus detection was comparatively lower (82.5%). Of the microorganisms, 85.2% were displayed in the first gallery of 30 images within row 5, 7.2% presented in the first gallery outside of row 5, and 7.6% presented in the hidden gallery of images. Among the 12 cases with missed diagnoses, 3 of 5 Candida spp and 3 of 7 HSV organisms were not presented within the 60 images selected by HGDDS. In another 6 cases, microorganisms were found within the 60 fields, but none were present in row 5. Very high sensitivity was observed for TPPTs across 3 of 4 common microorganisms on the HGDDS, although sensitivity was relatively lower for detecting HSV. Understanding morphologic patterns of various microorganisms in detection misses by the HGDDS may help guide the implementation of AI-assisted cervical cancer screening systems.
This study aimed to assess the shaping ability of 2 preparation systems with similar designs but different heat treatments during the progressive enlargement of severely curved mesiobuccal and mesiolingual canals in mandibular molars. Twenty-two mandibular molars with mesial canals curved >25º were scanned, pair-matched and divided into 2 groups (n=11) for preparation using austenitic (Genius) or martensitic (Genius Proflex) heat-treated instruments. Phase transformation temperatures of the instruments were determined via differential scanning calorimetry. Mesiobuccal and mesiolingual canals were first instrumented with a 17/.05 file and then sequentially enlarged using instruments sized 25/.04-40/.04, with scans taken after each enlargement step. Pre- and postoperative datasets were analysed for volume, surface area, unprepared canal areas, removed dentine volume, transportation and dentine thickness reduction. Statistical analyses included Student's t-test, Generalized Linear Model (GLM) multivariate model and independent t-tests (significance: P < .05). Genius displayed an austenitic structure, while Genius Proflex exhibited an R-phase structure at both 20ºC and 36ºC. Instrument size increase (P=.001) and heat treatment (P < .001) significantly affected shaping outcomes without interaction effects (P=.882). Size increases impacted canal volume (P=.023), unprepared areas (P=.005), removed dentine (P < .001) and dentine thickness reduction (P < .001), but not surface area (P=.390). Heat treatment significantly influenced unprepared canal areas (P < .001). Martensitic instruments caused less transportation (P < .05) and preserved more distal dentine (P < .001) compared to austenitic instruments. In severely curved mesial canals of mandibular molars, martensitic instruments result in fewer unprepared areas, reduced transportation and better dentine preservation at the distal root canal wall compared with austenitic instruments.
Intestinal ultrasound (IUS) is a non-invasive, accurate, and increasingly utilized tool for the assessment and monitoring of inflammatory bowel disease (IBD). This Australian survey, endorsed by the Gastroenterology Network of Intestinal Ultrasound (GENIUS), aimed to evaluate clinician attitudes toward IUS and identify barriers to its broader national implementation. National cross-sectional observational study. An online survey was distributed to adult and pediatric gastroenterologists and trainees across Australia, with data collected between September and December 2024. One hundred twenty-two respondents participated, comprising adult (52%), pediatric (25%), and trainee (23%) gastroenterologists, with two-thirds reporting a subspecialty interest in IBD. Nearly all agreed that IUS has clinical utility in Crohn's disease (99%) and ulcerative colitis (96%), with 96% considering IUS standard of care in IBD. Clinical confidence in IUS was high (84%), particularly among IBD specialists (95% vs 73%; p < 0.01), though lower than for colonoscopy (98%) and magnetic resonance enterography (MRE; 97%). IUS was also perceived as more resource-efficient than colonoscopy (96%) and MRE (88%). While 82% of respondents had access to IUS, mainly in an outpatient capacity, availability was lower in non-metropolitan locations. Among clinicians without access, almost all agreed that IUS access would improve IBD care; with scarcity of IUS funding and trained personnel cited as barriers. Almost half of the respondents had completed or were undertaking IUS training, with 40% of remaining respondents interested in future training. Australian gastroenterologists widely support IUS in IBD care. Expanding access to IUS requires renewed focus on service development and training initiatives, particularly in underserved areas, and cost-effectiveness studies to support these efforts. How Australian gastroenterologists view and use intestinal ultrasound in inflammatory bowel disease Patients with inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis, need close monitoring to assess disease activity and guide treatment decisions. Intestinal ultrasound (IUS) is a safe, accurate, and non-invasive imaging tool that allows clinicians to assess bowel inflammation without invasive and uncomfortable procedures such as colonoscopy and MRI. In this Australian survey, supported by the Gastroenterology Network of Intestinal Ultrasound (GENIUS), we asked gastroenterologists about their views on IUS. We received 122 responses from adult and pediatric gastroenterologists and trainees across Australia. Almost all respondents believed IUS is useful for managing IBD, and that access to IUS should be part of standard IBD care. Most felt confident using IUS to guide clinical decisions, and considered it more resource efficient than both colonoscopy and MRI. Although most respondents had access to IUS, availability was more limited in non metropolitan locations. Among respondents without access, almost all agreed that access to IUS would enhance the care of IBD patients. The main barriers identified were a lack of trained staff and funding to establish and support IUS service development. Encouragingly, nearly half of respondents had completed or were currently undertaking IUS training, and many others were interested in future training. Overall, there is strong support for the use of IUS in IBD care in Australia. Expanding access will require further investment in IUS training and service development.
Computer-aided detection (CADe) is anticipated to enhance adenoma detection rate (ADRs). The aim of this study was to systematically collect randomized-controlled trials comparing colonoscopy with CADe to standard colonoscopy without CADe in ADRs. We performed a Bayesian network meta-analysis of randomized-controlled trials. Three electronic databases including MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched. The primary outcome was the comparison of the performance of CADe systems in ADRs; the secondary outcome was the sessile serrated lesions detection rates (SSLDRs). A total of 48 randomized controlled trials involving 38,986 patients were included in the quantitative analysis. Several CADe systems improved ADR compared with controls that ENDO-AID (risk ratio [RR] 1.26, 95% credible interval [CrI] 1.14-1.40), CADEYE (RR 1.18, 95% CrI 1.10-1.26), and GI Genius (RR 1.15, 95% CrI 1.08-1.22) were supported by moderate confidence evidence according to the Confidence in Network Meta-Analysis (CINeMA). For SSLDR, ENDO-AID (RR 1.36, 95% CrI 1.03-1.79) and GI Genius (RR 1.25, 95% CrI 1.08-1.46) may offer improved detection compared with controls. Across multiple sensitivity analyses excluding studies by withdrawal time, conflicts of interest, limited study numbers, image-enhanced endoscopy, non-parallel design, single-center settings, operator experience, or earlier publication years, the direction and magnitude of ADR improvements with CADe systems remained largely consistent with the primary analysis. Based on the CINeMA framework, the certainty of evidence ranged from low to moderate, indicating that some CADe systems are likely to improve ADR.
Colorectal cancer (CRC) is a major global health issue, with adenomatous polyps as the main precursors. Standard colonoscopy is the gold standard, but its effectiveness is limited by operator variability and missed lesions. Artificial Intelligence (AI)-assisted colonoscopy is emerging to enhance detection, but a comparative performance across different AI systems is unclear. We conducted a systematic review and Bayesian network meta-analysis of 48 randomized controlled trials (RCTs) (N = 34 106 participants), searched across PubMed, Scopus, and Google Scholar up to November 4, 2025. We evaluated five commercial systems-EndoAngel, EndoAID, CAD-EYE, GI Genius, EndoScreener-and local platforms. Primary outcomes were Adenoma Detection Rate (ADR) and Adenomas Per Colonoscopy (APC). All AI systems significantly improved ADR versus conventional colonoscopy. EndoAngel showed the largest effect (OR 1.84; SUCRA 0.9), followed by EndoAID (OR 1.64; SUCRA 0.7). CAD-EYE (OR 1.46; SUCRA 0.5) and GI Genius (OR 1.45; SUCRA 0.5) also showed gains. APC gains were highest with EndoAID (MD 0.62). EndoAngel modestly increased withdrawal time (MD 1.14 min). Crucially, no AI system significantly improved the detection of high-risk lesions. Evidence quality was moderate. AI-assisted colonoscopy improves adenoma detection over conventional methods. While EndoAngel and EndoAID show the largest gains, performance differences among systems are modest. Detection of high-risk lesions remains uncertain, underscoring the need for future head-to-head trials and cost-effectiveness studies to guide optimal implementation in CRC screening.
Artificial intelligence (AI) is rapidly emerging as a transformative force in gastrointestinal (GI) and hepatopancreatobiliary (HPB) surgery, with the potential to enhance decision-making and surgical precision across the entire perioperative continuum. This narrative review explores the current applications and future directions of AI in GI and HPB surgical practice. We evaluated validated AI tools and platforms used in diagnostics, preoperative planning, intraoperative guidance, postoperative monitoring, and surgical education, while also considering the associated ethical and infrastructural challenges. AI-driven risk stratification models such as MySurgeryRisk and the POTTER calculator are improving preoperative prediction of complications. In endoscopy, systems like GI Genius have demonstrated improved adenoma detection rates, while EndoBRAIN-Plus enables real-time histologic assessment using high-resolution endocytoscopy. For surgical planning, virtual hepatectomy and three-dimensional reconstruction platforms such as HepaVision facilitate personalized and function-oriented liver resections. Intraoperatively, augmented reality and computer vision technologies provide enhanced anatomical visualization and guidance during minimally invasive procedures. AI-based predictive models are also contributing to improved postoperative monitoring by identifying patients at risk of complications such as anastomotic leaks. Furthermore, AI-driven simulation platforms are increasingly being integrated into surgical training and education. Overall, AI should be viewed not as a replacement for surgeons but as a powerful tool that can augment clinical judgment and improve surgical safety and personalization. However, challenges related to data standardization, model transparency, ethical governance, and clinical integration remain important barriers to widespread adoption.
The aim of this study was to determine an optimal method for performing quality control on Pap tests that adhere to CLIA '88 mandates that are applicable to employing an artificial intelligence (AI)-based system in routine practice. Four hundred ninety-seven archival ThinPrep Pap slides were retrieved and scanned with Genius Digital Diagnostics System. A cytologist and 3 cytopathologists (CP) performed initial interpretations (first read). Subsequently, all slides were rescanned and the new output was re-reviewed by another cytologist and the same 3 CP's after a washout period of 14 months (second read). The result of serial reads of each pathologist was compared with the original ThinPrep Interpretation (OTPI) and with each other. Interobserver concordance was calculated for all reads. Out of 497 cases 32.6%, 17.9%, 17.9%, 6.8%, 24.1%, and 0.6% were interpreted as negative for intraepithelial lesion or malignancy, atypical squamous cells of undetermined significance, low-grade squamous intraepithelial, atypical squamous cells, cannot exclude a high-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion/squamous cell carcinoma, and atypical glandular cell/adenocarcinoma in situ/adenocarcinoma per OTPI. Kendall's coefficient for concordance amongst the 3 CP's for serial reads were 0.924 and 0.892, respectively. Concordance between 4 diagnostic results (3 CP results and OTPI) for first and second reads was 0.902 and 0.865, respectively. Three hundred seven (61.8%), 310 (62.4%), and 328 (66%) cases were consistent when serial reads were compared for pathologist A, B, and C, respectively. Kappa values between reads ranged from 0.51 to 0.559 amongst the pathologists. Regular quality control checks that involve rescanning a subset of randomly selected slides, reanalyzing them with AI and multiple interpretations from CP's can be adopted for quality assurance for AI-assisted Pap test screening in the future after regulatory guidelines are updated.
Cytopathology is the first field of pathology in which artificial intelligence (AI) models were successfully developed and commercialized for routine clinical screening of cervical cytology, a practice that has been in place for the past 2 to 3 decades. However, the development and deployment of AI applications for nongynecologic cytology has just begun. The variety of cytology specimen types and preparations with associated unique characteristics presents technical challenges for the complete digitization of the cytology workflow. Despite of these challenges, a few institutions have adopted a complete digital cytology workflow. Technical advancement in digital cytopathology have replaced conventional rapid onsite evaluation by a variety of virtual telecytology systems. Novel digital diagnostic solutions for cytology are evolving. Among these, Hologic Genius is the only one approved by the Food and Drug Administration (FDA) for routine clinical screening of cervical cytology in the United States. The recommendations for AI validation and best-practice guidelines for digital cytopathology are currently being developed. Prospect of technical and AI advances in digital cytopathology include automation of sample preparation, ROSE using telecytology, automation of screening of gynecologic and nongynecologic cytology specimens, automated quantitation of biomarkers, quality control, and beyond. This review article uncovers recent advances in digital cytopathology and discusses potential use cases of AI applications for routine cytopathology practice in this modern era of digital cytopathology.
The '27 Club' myth masks a public health problem: systems that amplify musicians' psychological vulnerability. This multiple-case study uses reflexive thematic analysis of Janis Joplin, Kurt Cobain and Amy Winehouse, triangulating biographies, archives and documentaries. Across cases we identify a vulnerability triad - emotional dysregulation, chronic distress and substance-mediated coping - and show how 'tortured genius' narratives, industry pressures and fragmented care normalise risk. Cohort evidence indicates musicians face 1.7-3 times excess mortality for decades post-fame, especially solo artists and trauma survivors. We propose integrated risk assessments in contracts, mobile dual-diagnosis support and narrative interventions.