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Annals of Medicine Dec 2023The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The...
OBJECTIVE
The persistent spread of SARS-CoV-2 makes diagnosis challenging because COVID-19 symptoms are hard to differentiate from those of other respiratory illnesses. The reverse transcription-polymerase chain reaction test is the current golden standard for diagnosing various respiratory diseases, including COVID-19. However, this standard diagnostic method is prone to erroneous and false negative results (10% -15%). Therefore, finding an alternative technique to validate the RT-PCR test is paramount. Artificial intelligence (AI) and machine learning (ML) applications are extensively used in medical research. Hence, this study focused on developing a decision support system using AI to diagnose mild-moderate COVID-19 from other similar diseases using demographic and clinical markers. Severe COVID-19 cases were not considered in this study since fatality rates have dropped considerably after introducing COVID-19 vaccines.
METHODS
A custom stacked ensemble model consisting of various heterogeneous algorithms has been utilized for prediction. Four deep learning algorithms have also been tested and compared, such as one-dimensional convolutional neural networks, long short-term memory networks, deep neural networks and Residual Multi-Layer Perceptron. Five explainers, namely, Shapley Additive Values, Eli5, QLattice, Anchor and Local Interpretable Model-agnostic Explanations, have been utilized to interpret the predictions made by the classifiers.
RESULTS
After using Pearson's correlation and particle swarm optimization feature selection, the final stack obtained a maximum accuracy of 89%. The most important markers which were useful in COVID-19 diagnosis are Eosinophil, Albumin, T. Bilirubin, ALP, ALT, AST, HbA1c and TWBC.
CONCLUSION
The promising results suggest using this decision support system to diagnose COVID-19 from other similar respiratory illnesses.
Topics: Humans; COVID-19; Artificial Intelligence; SARS-CoV-2; COVID-19 Vaccines; COVID-19 Testing
PubMed: 37436038
DOI: 10.1080/07853890.2023.2233541 -
BMC Cancer Dec 2023The Syrian decade-long war has severely affected the healthcare system, including almost vanishing cancer screening practices, war-destroyed medical facilities, and lack...
BACKGROUND
The Syrian decade-long war has severely affected the healthcare system, including almost vanishing cancer screening practices, war-destroyed medical facilities, and lack of continuous medical education. This study aims to present data on the affected breast cancer screening practices, methods of diagnosis, and stages distribution in Syria.
METHODS
Medical charts of breast cancer patients treated at Albairouni University Hospital between January 2019 and May 2022 were retrospectively reviewed. Eligible patients were women diagnosed with primary breast cancer. Exclusion criteria included females receiving neoadjuvant chemotherapy and incomplete charts. Data regarding the patient's age, city of residence, marital status, number of children, smoking habits, method of diagnosis, tumor size (T), lymph nodes (N), and distal metastasis (M) were collected. We used Microsoft Excel and Statistical Package for the Social Sciences (SPSS) to analyze data. Descriptive methodology (frequency [n], percentage) was used.
RESULTS
The number of charts reviewed was 4,500. The number of remaining charts after applying the exclusion criteria was 2,367. The mean age was 51.8 (SD = 11.3). More than half of the patients (58.3%) came from outside Damascus -where the hospital is located- and its suburbs. Less than 5% of the population detected cancer by screening mammography. Only 32.4% of patients were diagnosed by a biopsy, while surgical procedures (lumpectomy and mastectomy) were used to diagnose 64.8% of the population. At the time of diagnosis, only 8% of patients presented with local-stage disease (stages 0 & I), 73% had a regional disease (stages II & III), and 19% had metastatic breast cancer (stage IV).
CONCLUSION
Our retrospective chart review analysis is the first comprehensive review in Syria for female breast cancer patients. We found a significant low percentage of patients diagnosed based on a screening mammogram, much higher surgical biopsies rather than a simple imaging-guided biopsy, and much lower than the national average of early-stage disease. Our alarming findings can serve as the base for future strategies to raise the population's health awareness, create more serious national screening campaigns, and adopt a multidisciplinary approach to the disease in Syria.
Topics: Child; Female; Humans; Middle Aged; Breast Neoplasms; Early Detection of Cancer; Mammography; Mastectomy; Neoplasm Staging; Retrospective Studies; Syria
PubMed: 38097985
DOI: 10.1186/s12885-023-11740-2 -
Archives of Pathology & Laboratory... Sep 2023Breast pathology has many mimics and diagnostic pitfalls. Evaluation of malignant breast lesions, particularly in the biopsy setting, can be especially challenging, with... (Review)
Review
CONTEXT.—
Breast pathology has many mimics and diagnostic pitfalls. Evaluation of malignant breast lesions, particularly in the biopsy setting, can be especially challenging, with diagnostic errors having significant management implications.
OBJECTIVE.—
To discuss the pitfalls encountered when evaluating ductal carcinoma in situ and invasive breast carcinomas, providing histologic clues and guidance for appropriate use and interpretation of immunohistochemistry to aid in the correct diagnosis.
DATA SOURCES.—
Data were obtained from review of pertinent literature of ductal carcinoma in situ and invasive breast carcinomas and from the experience of the authors as practicing breast pathologists.
CONCLUSIONS.—
Awareness of the pitfalls in diagnosing breast cancers is important when creating a differential diagnosis for each breast lesion evaluated. This review will cover some of these scenarios to aid in the diagnostic process.
Topics: Humans; Female; Breast Neoplasms; Carcinoma, Intraductal, Noninfiltrating; Biopsy, Large-Core Needle; Breast; Biopsy
PubMed: 37651393
DOI: 10.5858/arpa.2023-0007-RA -
Journal of Yeungnam Medical Science Oct 2023Recently, the International Working Group on the Diabetic Foot and the Infectious Diseases Society of America divided diabetic foot disease into diabetic foot infection...
Recently, the International Working Group on the Diabetic Foot and the Infectious Diseases Society of America divided diabetic foot disease into diabetic foot infection (DFI) and diabetic foot osteomyelitis (DFO). DFI is usually diagnosed clinically, while numerous methods exist to diagnose DFO. In this narrative review, the authors aim to summarize the updated data on the diagnosis of DFO. An extensive literature search using "diabetic foot [MeSH]" and "osteomyelitis [MeSH]" or "diagnosis" was performed using PubMed and Google Scholar in July 2023. The possibility of DFO is based on inflammatory clinical signs, including the probe-to-bone (PTB) test. Elevated inflammatory biochemical markers, especially erythrocyte sedimentation rate, are beneficial. Distinguishing abnormal findings of plain radiographs is also a first-line approach. Moreover, sophisticated modalities, including magnetic resonance imaging and nuclear medicine imaging, are helpful if doubt remains after a first-line diagnosis. Transcutaneous bone biopsy, which does not pass through the wound, is necessary to avoid contaminating the sample. This review focuses on the current diagnostic techniques for DFOs with an emphasis on the updates. To obtain the correct therapeutic results, selecting a proper option is necessary. Based on these numerous diagnosis modalities and indications, the proper choice of diagnostic tool can have favorable treatment outcomes.
PubMed: 37822082
DOI: 10.12701/jyms.2023.00976 -
Sensors (Basel, Switzerland) Oct 2023Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast...
Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer's disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD's convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis; the best results contrasted with several state-of-the-art diagnosis methods. In addition, our proposed SAMD algorithm can save about 19% of the convergence time on average in the AD diagnosis model compared with the gradient descent algorithms, which is very momentous in clinic.
Topics: Humans; Aged; Magnetic Resonance Imaging; Alzheimer Disease; Diagnosis, Computer-Assisted; Algorithms; Disease Progression; Neuroimaging
PubMed: 37960407
DOI: 10.3390/s23218708 -
Frontiers in Oncology 2023Pancreatic cystic neoplasms are increasingly diagnosed with the development of medical imaging technology and people's self-care awareness. However, two of their...
BACKGROUND
Pancreatic cystic neoplasms are increasingly diagnosed with the development of medical imaging technology and people's self-care awareness. However, two of their sub-types, serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN), are often misclassified from each other. Because SCN is primarily benign and MCN has a high rate of malignant transformation. Distinguishing SCN and MCN is challenging and essential.
PURPOSE
MRIs have many different modalities, complete with SCN and MCN diagnosis information. With the help of an artificial intelligence-based algorithm, we aimed to propose a multi-modal hybrid deep learning network that can efficiently diagnose SCN and MCN using multi-modality MRIs.
METHODS
A cross-modal feature fusion structure was innovatively designed, combining features of seven modalities to realize the classification of SCN and MCN. 69 Patients with multi-modalities of MRIs were included, and experiments showed performances of every modality.
RESULTS
The proposed method with the optimized settings outperformed all other techniques and human radiologists with high accuracy of 75.07% and an AUC of 82.77%. Besides, the proposed disentanglement method outperformed other fusion methods, and delayed contrast-enhanced T1-weighted MRIs proved most valuable in diagnosing SCN and MCN.
CONCLUSIONS
Through the use of a contemporary artificial intelligence algorithm, physicians can attain high performance in the complex challenge of diagnosing SCN and MCN, surpassing human radiologists to a significant degree.
PubMed: 37795452
DOI: 10.3389/fonc.2023.1181270 -
Scientific Reports Oct 2023Although the placement of an intraventricular catheter remains the gold standard method for the diagnosis of intracranial hypertension (ICH), the technique has several...
Although the placement of an intraventricular catheter remains the gold standard method for the diagnosis of intracranial hypertension (ICH), the technique has several limitations including but not limited to its invasiveness. Current noninvasive methods, however, still lack robust evidence to support their clinical use. We aimed to estimate, as an exploratory hypothesis generating analysis, the discriminative power of four noninvasive methods to diagnose ICH. We prospectively collected data from adult intensive care unit (ICU) patients with subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), and ischemic stroke (IS) in whom invasive intracranial pressure (ICP) monitoring had been placed. Measures were simultaneously collected from the following noninvasive methods: optic nerve sheath diameter (ONSD), pulsatility index (PI) using transcranial Doppler (TCD), a 5-point visual scale designed for brain Computed Tomography (CT), and two parameters (time-to-peak [TTP] and P2/P1 ratio) of a noninvasive ICP wave morphology monitor (Brain4Care[B4c]). ICH was defined as a sustained ICP > 20 mmHg for at least 5 min. We studied 18 patients (SAH = 14; ICH = 3; IS = 1) on 60 occasions with a mean age of 52 ± 14.3 years. All methods were recorded simultaneously, except for the CT, which was performed within 24 h of the other methods. The median ICP was 13 [9.8-16.2] mmHg, and intracranial hypertension was present on 18 occasions (30%). Median values from the noninvasive techniques were ONSD 4.9 [4.40-5.41] mm, PI 1.22 [1.04-1.43], CT scale 3 points [IQR: 3.0], P2/P1 ratio 1.16 [1.09-1.23], and TTP 0.215 [0.193-0.237]. There was a significant statistical correlation between all the noninvasive techniques and invasive ICP (ONSD, r = 0.29; PI, r = 0.62; CT, r = 0.21; P2/P1 ratio, r = 0.35; TTP, r = 0.35, p < 0.001 for all comparisons). The area under the curve (AUC) to estimate intracranial hypertension was 0.69 [CIs = 0.62-0.78] for the ONSD, 0.75 [95% CIs 0.69-0.83] for the PI, 0.64 [95%Cis 0.59-069] for CT, 0.79 [95% CIs 0.72-0.93] for P2/P1 ratio, and 0.69 [95% CIs 0.60-0.74] for TTP. When the various techniques were combined, an AUC of 0.86 [0.76-0.93]) was obtained. The best pair of methods was the TCD and B4cth an AUC of 0.80 (0.72-0.88). Noninvasive technique measurements correlate with ICP and have an acceptable discrimination ability in diagnosing ICH. The multimodal combination of PI (TCD) and wave morphology monitor may improve the ability of the noninvasive methods to diagnose ICH. The observed variability in non-invasive ICP estimations underscores the need for comprehensive investigations to elucidate the optimal method-application alignment across distinct clinical scenarios.
Topics: Adult; Humans; Middle Aged; Aged; Intracranial Pressure; Sensitivity and Specificity; Optic Nerve; Ultrasonography, Doppler, Transcranial; Intracranial Hypertension; Subarachnoid Hemorrhage; Ischemic Stroke; Ultrasonography
PubMed: 37891406
DOI: 10.1038/s41598-023-45834-5 -
Acta Cardiologica May 2024
Topics: Humans; Cardiovascular Diseases; Diagnostic Techniques, Cardiovascular; Disease Management
PubMed: 38770888
DOI: 10.1080/00015385.2024.2350765 -
BJGP Open Dec 2023Rather than first diagnosing and then deciding on treatment, GPs may intuitively decide on treatment and justify this through choice of diagnosis.
BACKGROUND
Rather than first diagnosing and then deciding on treatment, GPs may intuitively decide on treatment and justify this through choice of diagnosis.
AIM
To investigate the relationship between choice of a medicalising diagnosis and antibiotic treatment for throat-related consultations.
DESIGN & SETTING
A retrospective cohort study in a large database of UK electronic primary care records between 1 January 2010 and 1 January 2020.
METHOD
All first throat-related consultations were included, categorised as either pharyngitis/tonsillitis or sore throat. The outcome was any antibiotic prescription on the consultation date. GP-level random effects on prescribing and on diagnosis were estimated in a series of mixed-effects regression models, including age, sex, weekday, month, and clinician characteristics as fixed effects. GPs were grouped into quintiles by antibiotic prescribing propensity, and described the proportion of patients they diagnosed with pharyngitis/tonsillitis or sore throat in each quintile.
RESULTS
The analysis dataset included 393 590 throat-related consultations with 6881 staff. Diagnosis of pharyngitis/tonsillitis was strongly associated with antibiotic prescribing (adjusted odds ratio = 13.41, 95% confidence interval = 12.8 to 14.04). GP random effect accounted for 18% of variation in prescribing and for 26% of variation in diagnosis. GPs in the lowest quintile of antibiotic prescribing propensity diagnosed pharyngitis/tonsillitis on 31% of occasions, compared with 55% in the highest quintile.
CONCLUSION
There is substantial variation among GPs in diagnosis and treatment of throat-related problems. Preference for a medicalising diagnosis is associated with a preference for antibiotics, suggesting a common propensity to both diagnose and treat.
PubMed: 37429635
DOI: 10.3399/BJGPO.2023.0056 -
International Journal of Molecular... Sep 2023Histology diagnosis is essential for the monitoring and management of kidney transplant patients. Nowadays, the accuracy and reproducibility of histology have been...
Histology diagnosis is essential for the monitoring and management of kidney transplant patients. Nowadays, the accuracy and reproducibility of histology have been criticized when compared with molecular microscopy diagnostic system (MMDx). Our cohort included 95 renal allograft biopsies with both histology and molecular diagnoses. Discrepancies between histology and molecular diagnosis were assessed for each biopsy. Among the 95 kidney allograft biopsies, a total of 6 cases (6%) showed clear ( = 4) or borderline ( = 2) discrepancies between histology and molecular diagnoses. Four out of the six (67%) were cases with pathologically and clinically confirmed active infections that were diagnosed as mild to moderate T-cell-mediated rejection (TCMR) with MMDx. Two cases showed pathological changes that were not sufficient to make a definitive diagnosis of active rejection via histology, while MMDx results showed antibody-mediated rejection (ABMR). In addition, there were six cases with recurrent or de novo glomerular diseases diagnosed only via histology. All other biopsy results were in an agreement. Our results indicate that histology diagnosis of kidney allograft biopsy is superior to molecular diagnosis in the setting of infections and glomerular diseases; however, MMDx can provide helpful information to confirm the diagnosis of active ABMR.
Topics: Humans; Kidney Transplantation; Reproducibility of Results; Graft Rejection; Kidney Diseases; Biopsy; Antibodies; Kidney; Allografts
PubMed: 37762119
DOI: 10.3390/ijms241813817