Diagnostic Aid Research Articles

Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial.

Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning. To assess the effect of an LLM on physicians' diagnostic reasoning compared with conventional resources. A single-blind randomized clinical trial was conducted from November 29 to December 29, 2023. Using remote video conferencing and in-person participation across multiple academic medical institutions, physicians with training in family medicine, internal medicine, or emergency medicine were recruited. Participants were randomized to either access the LLM in addition to conventional diagnostic resources or conventional resources only, stratified by career stage. Participants were allocated 60 minutes to review up to 6 clinical vignettes. The primary outcome was performance on a standardized rubric of diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps, validated and graded via blinded expert consensus. Secondary outcomes included time spent per case (in seconds) and final diagnosis accuracy. All analyses followed the intention-to-treat principle. A secondary exploratory analysis evaluated the standalone performance of the LLM by comparing the primary outcomes between the LLM alone group and the conventional resource group. Fifty physicians (26 attendings, 24 residents; median years in practice, 3 [IQR, 2-8]) participated virtually as well as at 1 in-person site. The median diagnostic reasoning score per case was 76% (IQR, 66%-87%) for the LLM group and 74% (IQR, 63%-84%) for the conventional resources-only group, with an adjusted difference of 2 percentage points (95% CI, -4 to 8 percentage points; P = .60). The median time spent per case for the LLM group was 519 (IQR, 371-668) seconds, compared with 565 (IQR, 456-788) seconds for the conventional resources group, with a time difference of -82 (95% CI, -195 to 31; P = .20) seconds. The LLM alone scored 16 percentage points (95% CI, 2-30 percentage points; P = .03) higher than the conventional resources group. In this trial, the availability of an LLM to physicians as a diagnostic aid did not significantly improve clinical reasoning compared with conventional resources. The LLM alone demonstrated higher performance than both physician groups, indicating the need for technology and workforce development to realize the potential of physician-artificial intelligence collaboration in clinical practice. ClinicalTrials.gov Identifier: NCT06157944.

Assessing the impact of implantable loop recorder placement on anxiety level

Abstract Background Anxiety is a prolonged state of apprehension or worry that can be an independent predictor of cardiovascular events, associated with a 48% increased risk of cardiac death. About one-third of patients with arrhythmias present with high anxiety levels, and subsequently worse quality of life. In our experience of patient care, many patients feel anxious about the possibility of an arrhythmia that could lead to their demise, especially if their symptoms are undiagnosed. Purpose Our project aims to explore the change in patients’ anxiety levels following implantable loop recorder (ILR) placement. Methods This is a survey-based observational study that was performed in an outpatient clinic and hospital using validated General Anxiety Disorder-7 (GAD-7) questionnaires. Individuals who met indications to undergo ILR implantation including palpitations, syncope, subclinical atrial fibrillation, and cryptogenic stroke underwent baseline assessment of anxiety prior to implantation. Individuals had repeat assessments at 6 weeks and 12 weeks post-implantation to evaluate the effect of ILR placement. Results Our study included 50 individuals. Out of the initial subjects, 10/25 females and 11/25 males were lost to follow-up. The average initial GAD-7 score was 3.3 (Range 0, 15), average 6-week score was 1.6 (Range 0, 14), and average 12-week score was 0.9 (Range 0, 10). Total decrease in GAD-7 score after 6 weeks of having ILR was 50%. Total decrease in GAD-7 score after 12 weeks of ILR was statistically significant at 73% (P=0.03). Females showed greater improvement of GAD-7 scores in comparison to males at 6 weeks (67% vs 36%) and 12 weeks (81% vs 66%). Conclusion Studies have suggested an overall decreased level of anxiety in patients receiving implantable devices that have the potential to treat arrhythmias such as implantable cardiac defibrillators and pacemakers. Our results suggest that the benefit may extend to implantable devices that seek to provide further diagnostic information and aid in the prognostication of disease burden. As seen in our study population, GAD-7 scores showed dramatic improvement with greater percentage in female than male population. After the placement of loop recorders, many patients experience a sense of security with having a device that monitors them. Our study is the first to address the association between ILR and anxiety. Future studies evaluating this association would help increase the power of the study.Figure 1:Bar graph of GAD-7 Scores

Deep Learning Models for Anatomical Location Classification in Esophagogastroduodenoscopy Images and Videos: A Quantitative Evaluation with Clinical Data

Background/Objectives: During gastroscopy, accurately identifying the anatomical locations of the gastrointestinal tract is crucial for developing diagnostic aids, such as lesion localization and blind spot alerts. Methods: This study utilized a dataset of 31,403 still images from 1000 patients with normal findings to annotate the anatomical locations within the images and develop a classification model. The model was then applied to videos of 20 esophagogastroduodenoscopy procedures, where it was validated for real-time location prediction. To address instability of predictions caused by independent frame-by-frame assessment, we implemented a hard-voting-based post-processing algorithm that aggregates results from seven consecutive frames, improving the overall accuracy. Results: Among the tested models, InceptionV3 demonstrated superior performance for still images, achieving an F1 score of 79.79%, precision of 80.57%, and recall of 80.08%. For video data, the InceptionResNetV2 model performed best, achieving an F1 score of 61.37%, precision of 73.08%, and recall of 57.21%. These results indicate that the deep learning models not only achieved high accuracy in position recognition for still images but also performed well on video data. Additionally, the post-processing algorithm effectively stabilized the predictions, highlighting its potential for real-time endoscopic applications. Conclusions: This study demonstrates the feasibility of predicting the gastrointestinal tract locations during gastroscopy and suggests a promising path for the development of advanced diagnostic aids to assist clinicians. Furthermore, the location information generated by this model can be leveraged in future technologies, such as automated report generation and supporting follow-up examinations for patients.

Intelligent cell images segmentation system: based on SDN and moving transformer

Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially in regions with a scarcity of qualified healthcare professionals. This study aims to develop an intelligent system to enhance the diagnostic accuracy of cytopathology images by addressing image noise and segmentation issues, thereby improving the efficiency of medical professionals in disease diagnosis. We introduced an innovative system combining a self-supervised algorithm, SDN, for image denoising with data enhancement and image segmentation using the UPerMVit model. The UPerMVit model’s novel attention mechanisms and modular architecture provide higher accuracy and lower computational complexity than traditional methods. The proposed system effectively reduces image noise and accurately segments annotated images, highlighting cellular structures relevant to medical staff. This enhances diagnostic accuracy and aids in the accurate identification of pathological cells. Our intelligent system offers a reliable tool for medical professionals, improving diagnostic efficiency and accuracy in cytopathologic image analysis. It provides significant technical support in regions lacking adequate medical expertise.

Autism diagnostic aid (ADA) tool for use in adults with intellectual disability: development, face and content validation

Purpose Individuals with intellectual disability have an increased predisposition to other co-morbid physical health conditions, mental illness and neurodevelopmental conditions including autism spectrum condition. There are challenges in diagnosing autism in adults with intellectual disability, and these are often attributes to the complexities in symptoms. The purpose of this paper is to describe the development of the autism diagnostic aid (ADA) tool and face and content validation of its properties. Design/methodology/approach The ADA tool was developed following a detailed scoping exercise which included literature reviews, observations and discussion with wider multi-disciplinary team members. A face and content validation after consultation with eight experts who are trained and routinely perform assessments for autism spectrum disorders. The experts also have expertise in working with individuals with intellectual disability. Findings The experts agreed that the ADA tool is appropriate to the targeted construct and assessment objectives as a diagnostic aid in the assessment of autism in adults with intellectual disability. They also determined that the content of ADA tool was valid for its use as a diagnostic aid in the diagnosis of adults with intellectual disability. Research limitations/implications The ADA tool was considered a valid tool from the expert’s perspective. It was identified to have a good face and content validity. To the best of the authors’ knowledge, the ADA is first of its kind tool, developed specifically to aid clinicians with autism assessments in adults with intellectual disability. Practical implications This tool could potentially help with timely assessment and help with providing appropriate intervention but would require further exploration. Originality/value To the best of the authors’ knowledge, this is the first of its kind tool for the assessment of autism in adults with intellectual disability.

Characteristics and Prognosis of "Acute Promyelocytic Leukemia-like" Nucleophosmin-1-Mutated Acute Myeloid Leukemia in a Retrospective Patient Cohort.

Background: AML with NPM1 mutation is the largest subcategory of AML, representing about 35% of AML cases. It is characterized by CD34 negativity, which suggests a relatively differentiated state of the bulk of leukemic blasts. Notably, a significant subset of NPM1-mutated AML cases also exhibit HLA-DR negativity, classifying them as "double-negative", and mimicking, therefore, the CD34- HLA-DR- immunophenotype of acute promyelocytic leukemia (APL). Objectives: This study focuses on the "acute promyelocytic leukemia-like" ("APL-like") subset of NPM1-mutated AML, which can be challenging to distinguish from APL at presentation, prior to confirming RARa translocations. We aim to investigate the hematologic and immunophenotypic parameters that may aid to its distinction from APL. Additionally, we explore differences in genetic profile and prognosis between "APL-like" and "non-APL-like" NPM1-mutated AML cases. Methods: We conducted a retrospective evaluation of 77 NPM1-mutated AML cases and 28 APL cases. Results: Morphological characteristics, hematologic parameters (such as DD/WBC and PT/WBC), and specific immunophenotypic markers (including SSC, CD64, and CD4) can assist in the early distinction of "APL-like" NPM1-mutated AML from APL. Regarding differences in genetic profiles and outcomes between "APL-like" and non-"APL-like" NPM1-mutated AML cases, we observed a significantly higher incidence of IDH1/2 /TET2 mutations, along with a significantly lower incidence of DNMT3A mutations in the "APL-like" subset compared to the non-"APL-like" subset. The frequency of Ras-pathway and FLT3 mutations did not differ between these last two groups, nor did their prognoses. Conclusions: Our findings contribute to a comprehensive characterization of NPM1-mutated AML, enhancing diagnostic accuracy and aiding in the detailed classification of the disease. This information may potentially guide targeted therapies or differentiation-based treatment strategies.

A CASE REPORT OF CHRONIC HEPATITIS WITH EFFUSION IN A DOG AND ITS THERAPEUTIC MANAGEMENT

The present study was aimed to know the chronic hepatitis with abdominal effusion in a beagle dog and its therapeutic management. A 2.5 year old male beagle was presented to Madras Veterinary College teaching Hospital, madras veterinary college, Chennai with the history of inappetence, abdominal distension for past 10 days and the clinical signs of inappetence, fever and palpablelymph node and congested mucous membrane. On Physical examination, dog showed respiratory distress and dull. Dog was subjected for radiography examination, haematology, biochemistry parameters and ultrasound examination. On first day of examination, dog was treated with Lasix at the dose rate of 3 mg/kg body weight, antibiotics and Tribivet injection. Hematology and biochemical values revealed neutrophilia, elevated alkaline phosphatase, total bilirubin, direct bilirubin, and decreased total protein and albumin. The lateral view radiography of abdomen revealed fluid shadow and ultrasound examination revealed that chronic hepatitis with moderate abdominal effusion. In case of clinical suspicion of hepatitis an ultrasonographic liver scan can be of diagnostic aid and the finding of an altered structure can be indicative of a liver problem. In many cases of chronic hepatitis may not be associated with ultrasound abnormalities but ascites can be easily identified. Dog was treated with antibiotic cefotaxime at the dose rate of 25 mg/Kg body weight and Lasix at the dose rate of 3 mg/Kg body weight with liver supplement of Lysibin which contain silymarin and supplement with Astymin syrup for 3 weeks. The animal was reviewed after three weeks of treatment which was subjected for haeamtology, biochemistry and ultrasound examination which revealed there was improvement in the blood parameters with decreased total protein and normal level of Alkaline phosphatase and bilirubin level and ultrasound examination revealed normal study of abdominal effusion and liver echogenicity and dog had normal appetite and normal vital parameters. In this case hepatitis may be due to any viral etiology like adenovirus that may cause hepatitis with effusion that may be respond to treatment and diuretics and brought normal parameters of vital in dog. Chronic hepatitis with effusion may be further confirmed by Liver biopsy and molecular diagnosis.

The Role of Confirmatory Testing in Carpal Tunnel Syndrome: Electrodiagnostic Study, Ultrasound and CTS-6.

Carpal tunnel syndrome (CTS) is the most common upper extremity compressive neuropathy. The reference standard for the diagnosis of CTS remains an area of controversy. The diagnosis can be established clinically, but options for confirmatory testing include electrodiagnostic studies, ultrasound and diagnostic aids such as the CTS-6 score. This review article summarises the current evidence for each confirmatory testing modality, contrasts their advantages and disadvantages and discusses future directions for investigation. Level of Evidence: Level V (Diagnostic).

Engineering of Kidney-Targeting Fluorophores with Tunable Emission from NIR-I to NIR-II for Early Diagnosis of Kidney Disease.

The development of rapidly distributed and retained probes within the kidneys is important for accurately diagnosing kidney diseases. Although molecular imaging shows the potential for non-intrusively interrogating kidney disease-related biomarkers, the limited kidney contrast of many fluorophores, owing to their relatively low distribution in the kidney, hinders their effectiveness for kidney disease detection. Herein, for the first time, an amino-functionalization strategy is proposed to construct a library of kidney-targeting fluorophores NHcy with tunable emissions from NIR-I to NIR-II. Among these, NHcy-8 is the first small-molecule NIR-II dye without a renal clearance moiety, designed specifically for kidney-targeting imaging. Building on this class of NIR-II fluorophore, the first NIR-II small-molecule kidney-targeting pH probe NIR-II-pH is developed, which exhibits a desirable kidney distribution after intravenous injection and is fluorescent only after activation by acidosis. NIR-II in vivo fluorescence/photoacoustic imaging of kidney disease models induced by cisplatin and renal I/R injury using NIR-II-pH reveals increasingly severe metabolic acidosis as the disease progressed, enabling sensitive detection of the onset of acidosis 36h (cisplatin group) earlier than clinical methods. Thus, this study introduces a practical NIR-II kidney-targeting probe and provides a useful molecular blueprint for guiding kidney-targeting NIR-II fluorophores as diagnostic aids for kidney diseases.

9390 Does Znt8a Have An Association With The Duration Of Diabetes? Case Series And Literature Review

Abstract Disclosure: N.M. Rivera Vargas: None. C.S. Botero Suarez: None. S.K. Suryanarayanan: None. S. Saad-Omer: None. Context: Latent autoimmune Diabetes of adults (LADA) is a form of autoimmune diabetes with features of Type 2 Diabetes but positive autoantibodies resembling Type 1 Diabetes. Latent autoimmune diabetes in adults (LADA), has a mixed phenotype with positive islet-cell antibodies (ICA), glutamic acid decarboxylase autoantibodies (GADA), protein tyrosine phosphatase autoantibodies (IA2A), insulin autoantibodies (IAA), or zinc transporter-8 autoantibodies (ZnT8A). Since the discovery of ZnT8A, the relevance and clinical utility of this marker has sparked interest. Although ZnT8A is used as a complementary diagnostic aid in children, its utility in adults seems to be unclear. Objective: We report a case series of 26 subjects referred to the Orlando VA Endocrine clinic with measured ZnT8A. We hypothesize that higher ZnT8A will be found in adult autoimmune diabetes with newly diagnosed diabetes rather than those with a longer duration of diabetes. We also explore the association between ZnT8A and family history, duration of diabetes, diabetes complications, and C-peptide levels. Method: A case series of 26 adult patients with diabetes mellitus with a positive ZnT8A (≥15 U/mL). We reviewed medical records of these cases to include ZnT8A levels, years of diabetes diagnosis, C-peptide levels, and diabetes complications. Patients were classified into subgroups based on ZnT8A levels into < 350 U/mL or ≥ 350 U/mL. Results: The 26 patients reviewed had a mean (SD) age of 46 (13) years. The mean (SD) duration of diabetes was 16 (12) years, and HbA1C was 8.4 (1.1) %. Poisson regression analysis showed a statistically significant inverse relationship between ZnT8A and years of diabetes (regression coefficient: -0.028; 95% confidence interval: -0.03 to -0.25; p-value <0.001). Diabetic ketoacidosis was present in 35% of subjects, while diabetic neuropathy was noted in 58% of subjects with positive ZnT8A. Patients with a high ZnT8Ab above 350 U/mL displayed a weak trend towards a lower C-peptide mean (p-value 0.42), and lower prevalence of DKA (p-value 0.42) which was not statistically significant. Conclusion: In this case series of patients with adult-onset diabetes, there seems to be a statistically significant inverse relationship between ZnT8A levels and duration of diabetes suggesting a reduction of ZnT8A levels over time. Although limited by a small number of subjects, this may suggest the need for screening with autoantibodies in the setting of diabetes earlier in the disease process. Presentation: 6/2/2024

B-080 Development and Validation of a GQ1b IgG ELISA as a Diagnostic Aid for Subacute Demyelinating Polyneuropathies

Abstract Background Autoimmunity targeting GQ1b is associated with group of disorders that includes Miller-Fisher Syndrome (MFS), Bickerstaff Brainstem Encephalitis (BBE) and classic Guillain Barre Syndrome (GBS) with ophthalmoplegia. Collectively these are referred to as GQ1B-IgG related syndromes. The prevalence of GQ1b-IgG in this population of patients is high and has been reported to be >80% in well-defined clinical cohorts. Clinical phenotypic presentation is diverse and requires highly accurate antibody testing. The objective of this study was to evaluate a newly developed custom GQ1b-IgG assay, using a semi-quantitative ELISA-based method. Methods A GQ1b-IgG ELISA was developed and validated. Patient sera along with assay controls and a single-point calibrator are diluted and incubated in duplicate wells in a 96-well plate coated with GQ1b antigen. The plate is washed and incubated with anti-human-IgG secondary antibody conjugated with HRP. After a colorimetric reaction, optical densities (OD) are read. The OD of patient samples are compared against the calibrator, generating a cut-off index (COI). Samples ≥1.0 COI are considered positive. During the validation phase, analytical and clinical performance characteristics were evaluated. Studies included assessment of imprecision at multiple concentrations of GQ1b-IgG, accuracy using method comparison, analytical sensitivity and specificity, reference range, common interferences, plate drift, and clinical performance in relevant disease and control cohorts. Results Intra-assay imprecision (coefficient of variation; %CV) ranged from 1.0 - 16% across all concentrations of GQ1b-IgG. Inter-assay imprecision (%CV) ranged from 12.4 - 44.5% but was <20% at or above the assay cut-off. We found perfect qualitative agreement with an independent ELISA reference method (20 positive and 20 negative samples selected based on reference method results [reference interval <1:100]). Limit of detection (LOD) was calculated at a COI of 0.22. To assess analytical specificity, 40 samples with high concentrations of immunoglobulins (hypergammaglobulinemia [N=15], IgG M-protein [N=15] and systemic lupus erythematosus [N=10] were tested and all were negative. A reference interval study was performed on 140 healthy donor serums, all were negative. Interference studies were conducted on 2 positive and 2 negative serum samples, spiked for highly hemolytic (1000 mg/dL), lipemic (2000 mg/dL) and icteric (60 mg/dL) conditions. There was no impact on the assay even with high levels of a given interferent. Plate drift on 2 distinct patient pools tested across the plate (COI range 0.7 - 1.3) had overall plate CV’s ≤10%. ROC analysis was performed on a cohort of 278 patient samples (15 MFS cases and 263 controls [suspected demyelinating neuropathy or disease mimics]). A cut-off value of 1.0 (COI) was selected based on the Youden index. At this cut-off, clinical sensitivity for MFS was 87% while clinical specificity was 99.2%. Conclusions The observed performance of the GQ1b-IgG ELISA was acceptable for clinical laboratory implementation. A COI of ≥ 1.0 was selected for clinical use.

54VP Emerging muscle ultrasound patterns as a diagnostic aid in TTN-related myopathy

54VP Emerging muscle ultrasound patterns as a diagnostic aid in TTN-related myopathy

Deciphering ovarian torsion: insights from CT imaging analysis.

In the milieu of emergency medicine, pelvic and lower abdominal pain present recurrently, with ovarian torsion posing a formidable diagnostic quandary amid multifarious etiologies. Given the burgeoning reliance on CT in acute care settings, it invariably assumes primacy as the principal imaging modality. This study endeavors to elucidate the CT imaging manifestations encountered by surgically confirmed ovarian torsion patients and utilizing CT to differentiate necrosis. A retrospective analysis (January, 2015- April, 2019) utilizing hospital archives was conducted on patients diagnosed with ovarian torsion, post-surgery. Inclusion criteria encompassed patients who underwent CT examinations within one week of diagnosis. A large array of CT findings encompassing midline orientation, uterine deviation, intraovarian hematoma/mass, and multiple others were systematically documented. 90 patients were diagnosed with ovarian torsion- 53 (59%) had CT within one week of diagnosis, 41(77%) underwent a CT with IV contrast and 12 (23%) without IV contrast. Mean age was 43 years (range 19-77 years), with near equal distribution of involvement of each ovary. Mean maximum ovarian diameter was 11.7 ± 6.3cm (4.2-34.8cm). Most common imaging features include the presence of thickened pedicle (43/53, 81%), midline ovary (41/53, 77%), presence of thickened fallopian tube (31/49, 63%), and ipsilateral uterine deviation (33/53, 62%). Based on contemporaneous imaging report, torsion was diagnosed in 25/ 53 studies giving a sensitivity of 47%. Enlarged ovarian dimensions (> 3.0cm), thickened vascular pedicle or fallopian tube, midline ovarian disposition with ipsilateral uterine deviation, and the presence of a whirlpool sign emerged as predominant CT imaging features in surgically confirmed ovarian torsion cases, serving as pivotal diagnostic aides for radiologists. Concomitant pelvic free fluid and intraovarian hematoma signify necrotic changes, indicative of ischemic severity and disease progression.

A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey.

The identification of concussion risk factors, such as visible signs and mechanisms of injury, improves concussion identification. Exploring individual risk factors, such as concussion history, may help to improve existing concussion risk models and algorithms. The primary aim of the current study was to use machine learning techniques to develop a comprehensive, prospectively coded concussion risk model in professional hockey among players exhibiting visible signs. The secondary aim was to examine whether including concussion history improves model performance. Data from the National Hockey League (NHL) spotter program, including coded visible signs and mechanisms of injury associated with possible concussive events, were extracted from the 2018-2019 to the 2021-2022 seasons. Each unique spotter event was matched with data extracted from the medical record to determine whether the event was associated with a subsequent physician diagnosed concussion. We compared the ability of three machine learning-based approaches to identify the likelihood of physician diagnosed concussion: conditional inference tree, conditional inference random forest, and logistic regression. A total of 1563 unique events with visible signs were identified by spotters (183 leading to a concussion diagnosis). A randomly selected training sample had 1250 events (146 concussions) and the remaining set-aside test sample had 313 events (37 concussions). The obtained models performed at a high level with large effects in the training [area under the receiver operating characteristic curve (AUC) = 0.79] and set-aside test data (AUC = 0.82). Concussion history was retained in the tree and logistic regression models, with each additional prior concussion associated with a 1.32 times increased odds of concussion diagnosis. We present simple tree and logistic algorithms for concussion screening and as diagnostic aids. Our results show that player concussion history can explain additional risk above and beyond that explained by visible signs and mechanisms of injury alone.

D2-40 and CK17 Immunohistochemistry as a Diagnostic Adjunct for HPV-Independent Squamous Lesions in the Vulva and Their Role in Defining Atypical Lichen Sclerosus.

Vulvar lichen sclerosus (LS) is a common, chronic inflammatory disorder with a subset of cases progressing to differentiated vulvar intraepithelial neoplasia (dVIN) and/or squamous cell carcinoma (SCC). Histopathologic diagnosis of LS and dVIN can be challenging, and it is difficult to predict the subset of LS cases that progress. Immunohistochemistry (IHC) may be a useful diagnostic aid in this setting. CK17 has been shown to be overexpressed in invasive SCC and dVIN, and less commonly in LS. Similar to CK17, D2-40 has been correlated with cutaneous SCC prognosis but has not been evaluated in vulvar lesions. We identified a total of 13 patients with HPV-independent vulvar SCC that had precursor LS or dVIN. CK17 and D2-40 IHC stain intensity and pattern was scored in foci of LS, dVIN, and SCC. An increase in basal layer D2-40 expression was observed with progression from LS to dVIN with strong and diffuse staining in SCC. CK17 maintained similar stain intensity among squamous lesions, but displayed different patterns of staining, with superficial staining in LS, suprabasal staining in dVIN, and diffuse staining in SCC. A subset of LS cases displayed an intermediate (suprabasal) CK17 IHC profile, wild-type p53 expression, and cytomorphologic and architectural features intermediate between LS and dVIN; we defined such cases as "atypical LS." We found that a panel of D2-40/CK17 can serve as a diagnostic adjunct to differentiate LS, dVIN, and invasive SCC. Additional studies with larger patient cohorts are needed to validate these findings and determine their prognostic significance.

Modern diagnostic aids: Exploring advanced tools

Orthodontic diagnosis and treatment planning have evolved in recent years making it more reliable and easy clinical tool. Recent diagnostic tools are issue oriented. Orthodontic treatment is carried out in all three dimensions, but we were having diagnostic aids to examine patient in two dimensions. But, now the concept of orthodontic treatment have changed w.r.t aesthetics and function. The recent advancement of digital technologies is 3D imaging making it more dynamic. The 3 dimensional diagnostic tools include digital scanning, 3D models, digital radiographic records and analysis, CBCT, CAD/CAM technology and 3D printers. The accuracy has been increased. Diagnostic capabilities of the imaging tools have been improved with digital processes, making orthodontic treatment more efficient, accurate and comfortable as compared to past. This article aims to give an insight into recent technology in diagnostic tools.

Dysbiosis of pro-inflammatory and anti-inflammatory salivary cytokines during psoriasis providing a therapeutic window and a valuable diagnostic aid in future.

The objective of this article is to evaluate the salivary levels of tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), interleukin-2 (IL-2), and IL-10 in patients with active psoriasis and compare them with those in healthy control subjects. This study included 60 subjects who were clinically diagnosed cases with active psoriasis (categorized further into 33 mild to moderate and 27 severe cases based on the Psoriasis Area Severity Index score) and 60 age- and gender-matched healthy control subjects. Levels of TNF-α, IFN-γ, IL-2, and IL-10 in the unstimulated saliva of subjects were determined via enzyme-linked immunosorbent assay (BT Lab). The salivary levels of TNF-α, IFN-γ, and IL-2 were significantly higher, whereas IL-10 concentration was significantly reduced in psoriatic patients in comparison to controls, and the difference increased with the progressing severity of the disease. Assessment of cytokine profiles in psoriasis patients is significant for diagnostic validation and monitoring the disease severity. Saliva offers an alternate, noninvasive, and readily available biological sample for evaluating cytokine levels. Extensive research in this field has been recommended for better scientifically proven conclusions.

Neonatal Infectious Disease: A Major Contributor to Infant Mortality Requiring Advances in Point-of-Care Diagnosis.

Neonatal infectious disease continues to result in high rates of infant morbidity and mortality. Early- and late-onset disease represent difficult to detect and difficult to treat illnesses, particularly when antimicrobial resistant pathogens are present. Newborns are immunodeficient and are at increased risk of vertical and horizontal infection, with preterm infants increasingly susceptible. Additional risk factors associated with infection include prolonged use of a central catheter and/or ventilation, congenital abnormalities, admittance to intensive care units, and the use of broad-spectrum antibiotics. There is increasing recognition of the importance of the host microbiome and dysbiosis on neonatal infectious disease, including necrotising enterocolitis and sepsis in patients. Current diagnostic methods rely on blood culture, which is unreliable, time consuming, and can result in false negatives. There is a lack of accurate and reliable diagnostic tools available for the early detection of infectious disease in infants; therefore, efficient triage and treatment remains challenging. The application of biomarkers, machine learning, artificial intelligence, biosensors, and microfluidics technology, may offer improved diagnostic methodologies. Point-of-care devices, such diagnostic methodologies, may provide fast, reliable, and accurate diagnostic aids for neonatal patients. This review will discuss neonatal infectious disease as impacted by antimicrobial resistance and will highlight novel point-of-care diagnostic options.

Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.

Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However, the use of artificial intelligence by trainees across different levels of experience has not been well-studied. This study investigates the impact of artificial intelligence assistance on the diagnostic accuracy for intracranial hemorrhage and large-vessel occlusion by medical students and resident trainees. This prospective study was conducted between March 2023 and October 2023. Medical students and resident trainees were asked to identify intracranial hemorrhage and large-vessel occlusion in 100 noncontrast head CTs and 100 head CTAs, respectively. One group received diagnostic aid simulating artificial intelligence for intracranial hemorrhage only (n = 26); the other, for large-vessel occlusion only (n = 28). Primary outcomes included accuracy, sensitivity, and specificity for intracranial hemorrhage/large-vessel occlusion detection without and with aid. Study interpretation time was a secondary outcome. Individual responses were pooled and analyzed with the t test; differences in continuous variables were assessed with ANOVA. Forty-eight participants completed the study, generating 10,779 intracranial hemorrhage or large-vessel occlusion interpretations. With diagnostic aid, medical student accuracy improved 11.0 points (P < .001) and resident trainee accuracy showed no significant change. Intracranial hemorrhage interpretation time increased with diagnostic aid for both groups (P < .001), while large-vessel occlusion interpretation time decreased for medical students (P < .001). Despite worse performance in the detection of the smallest-versus-largest hemorrhages at baseline, medical students were not more likely to accept a true-positive artificial intelligence result for these more difficult tasks. Both groups were considerably less accurate when disagreeing with the artificial intelligence or when supplied with an incorrect artificial intelligence result. This study demonstrated greater improvement in diagnostic accuracy with artificial intelligence for medical students compared with resident trainees. However, medical students were less likely than resident trainees to overrule incorrect artificial intelligence interpretations and were less accurate, even with diagnostic aid, than the artificial intelligence was by itself.

Precision Medicine With Data-driven Approaches: A Framework For Clinical Translation

Precision medicine-based approaches differentiate themselves by taking into consideration subpopulation variability (e.g. genetic variations, age, gender, race, addictions). To date, traditional models, such as Trial-and-Error Dosing, Empirical Treatment Guidelines, Statistical and Actuarial Models, Pathophysiological Models, and Clinical Judgment and Experience, have been generalized in healthcare fields. However, more comprehensive and innovative technologies are required besides these conventional modelings, which are subject to various limitations such as low efficiency and incapability of processing complex biological systems. Here we review diverse machine learning (ML) algorithms integrated with big data and omics and its applications in various aspects of precision medicine. ML is the branch of artificial intelligence (AI), which has been rapidly developed and highlighted as a promising method to decrease diagnostic errors and aid clinicians with decision-making in recent decades. We focused on applications of ML models such as support vector machine (SVM), K Nearest Neighbor (KNN) random forest (RF), convolutional neural networks (CNNs) and deep learning in drug toxicity prediction, cardiovascular diseases, neurodegenerative diseases, and cancer therapies within precision medicine and specific benefits and challenges of each. This review provides insights on the wider utilization in clinical environments by recognizing current advantages that are expected to expand the scope of AI-driven methods and issues that need to be addressed for further studies.