The value of S-Detect in improving the diagnostic performance of radiologists for the differential diagnosis of thyroid nodules

Aims: To compare the diagnostic value of S-Detect (a computer aided diagnosis system using deep learning) in differentiating thyroid nodules in radiologists with different experience and to assess if S-Detect can improve the diagnostic performance of radiologists.Materials and methods: Between February 2018 and October 2019, 204 thyroid nodules in 181 patients were included. An experienced radiologist performed ultrasound for thyroid nodules and obtained the result of S-Detect. Four radiologists with different experience on thyroid ultrasound (Radiologist 1, 2, 3, 4 with 1, 4, 9, 20 years, respectively) analyzed the conventional ultrasound images of each thyroid nodule and made a diagnosis of “benign” or “malignant” based on the TI-RADS category. After referring to S-Detect results, they re-evaluated the diagnoses. The diagnostic performance of radiologists was analyzed before and after referring to the results of S-Detect.Results: The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of S-Detect were 77.0, 91.3, 65.2, 68.3 and 90.1%, respectively. In comparison with the less experienced radiologists (radiologist 1 and 2), S-Detect had a higher area under receiver operating characteristic curve (AUC), accuracy and specificity (p <0.05). In comparison with the most experienced radiologist, the diagnostic accuracy and AUC were lower (p<0.05). In the less experienced radiologists, the diagnostic accuracy, specificity and AUC were significantly improved when combined with S-Detect (p<0.05), but not for experienced radiologists (radiologist 3 and 4) (p>0.05).Conclusions: S-Detect may become an additional diagnostic method for the diagnosis of thyroid nodules and improve the diagnostic performance of less experienced radiologists.

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Diagnostic accuracy of a urine dipstick for detecting albuminuria in hypertensive patients

F1000Research ◽

10.12688/f1000research.25564.3 ◽

2021 ◽

Vol 9 ◽

pp. 1244

Author(s):

Phornwipa Panta ◽

Win Techakehakij

Keyword(s):

Diagnostic Accuracy ◽

Predictive Value ◽

Operating Characteristic ◽

Characteristic Curve ◽

Hypertensive Patients ◽

Spot Urine ◽

Urine Dipstick ◽

Urine Albumin ◽

Low Sensitivity ◽

Sensitivity Specificity

Background: Screening for albuminuria is generally recommended among patients with hypertension. While the urine dipstick is commonly used for screening urine albumin, there is little evidence about its diagnostic accuracy among these patients in Thailand. This study aimed to assess the diagnostic accuracy of a dipstick in Thai hypertensive patients for detecting albuminuria. Methods: This study collected the data of 3,067 hypertensive patients, with the results of urine dipstick and urine albumin-to-creatinine ratio (ACR) from random single spot urine being examined in the same day at least once, at Lampang Hospital, Thailand, during 2018. For ACR, a reference standard of ≥ 30 mg/g was applied to indicate the presence of albuminuria. Results: The sensitivity, specificity, positive predictive value (PPV), and negative predictive value of the trace result from dipsticks were 53.6%, 94.5%, 86.5%, and 75.5%, respectively. The area under the receiver operating characteristic curve of the dipstick was 0.748. Conclusion: Using the dipstick for screening albuminuria among hypertensive patients should not be recommended for mass screening due to its low sensitivity. In response to high PPV, a trace threshold of the dipstick may be used to indicate presence of albuminuria.

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AIBx, artificial intelligence model to risk stratify thyroid nodules

10.1101/2021.02.13.21251688 ◽

2021 ◽

Author(s):

Johnson Thomas ◽

Tracy Haertling

Keyword(s):

Artificial Intelligence ◽

Positive Predictive Value ◽

Negative Predictive Value ◽

Predictive Value ◽

Thyroid Nodules ◽

Classification Systems ◽

Image Similarity ◽

Ultrasound Images ◽

Similarity Model ◽

Sensitivity Specificity

AbstractBackgroundCurrent classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. 1 out of 2 women over the age of 50 may have a thyroid nodule and at present the only way to exclude malignancy is through invasive procedures. Hence, there exists a need for noninvasive objective classification of thyroid nodules. Some cancers have benign appearance on ultrasonogram. Hence, we decided to create an image similarity algorithm rather than image classification algorithm.MethodsUltrasound images of thyroid nodules from patients who underwent either biopsy or thyroid surgery from February of 2012 through February of 2017 in our institution were used to create AI models. Nodules were excluded if there was no definitive diagnosis of benignity or malignancy. 482 nodules met the inclusion criteria and all available images from these nodules were used to create the AI models. Later, these AI models were used to test 103 thyroid nodules which underwent biopsy or surgery from March of 2017 through July of 2018.ResultsNegative predictive value of the image similarity model was 93.2%. Sensitivity, specificity, positive predictive value and accuracy of the model was 87.8%, 78.5%, 65.9% and 81.5% respectively.ConclusionWhen compared to published results of ACR TIRADS and ATA classification system, our image similarity model had comparable negative predictive value with better sensitivity specificity and positive predictive value. By using image similarity AI models, we can eliminate subjectivity and decrease the number of unnecessary biopsies. Using image similarity AI model, we were able to create an explainable AI model which increases physician’s confidence in the predictions.

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Diagnostic accuracy of a urine dipstick for detecting albuminuria in hypertensive patients

F1000Research ◽

10.12688/f1000research.25564.1 ◽

2020 ◽

Vol 9 ◽

pp. 1244

Author(s):

Phornwipa Panta ◽

Win Techakehakij

Keyword(s):

Diagnostic Accuracy ◽

Predictive Value ◽

Operating Characteristic ◽

Characteristic Curve ◽

Hypertensive Patients ◽

Spot Urine ◽

Urine Dipstick ◽

Urine Albumin ◽

Low Sensitivity ◽

Sensitivity Specificity

Background: Screening for albuminuria is generally recommended among patients with hypertension. While the urine dipstick is commonly used for screening urine albumin, there is little evidence about its diagnostic accuracy among these patients. This study aimed to assess the diagnostic accuracy of a dipstick in Thai hypertensive patients for detecting albuminuria. Methods: This study collected the data of 3,067 hypertensive patients, with the results of urine dipstick and urine albumin-to-creatinine ratio (ACR) from random single spot urine being examined in the same day at least once, at Lampang Hospital, Thailand, during 2018. For ACR, a reference standard of ≥ 30 mg/g was applied to indicate the presence of albuminuria. Results: The sensitivity, specificity, positive predictive value (PPV), and negative predictive value of the trace result from dipsticks were 53.6%, 94.5%, 86.5%, and 75.5%, respectively. The area under the receiver operating characteristic curve of the dipstick was 0.748. Conclusion: Using the dipstick for screening albuminuria among hypertensive patients should not be recommended due to its low sensitivity. In response to high PPV, a trace threshold of the dipstick may be used to indicate presence of albuminuria.

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Comparative Analysis of Diagnostic Value for Shear Wave Elastography and Real-Time Elastographic Imaging for Thyroid Nodules

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2019.2594 ◽

2019 ◽

Vol 9 (2) ◽

pp. 334-338

Author(s):

Qing Yang ◽

Wenhong Zhou ◽

Jiyu Li ◽

Guojun Wu ◽

Feng Ding ◽

...

Keyword(s):

Differential Diagnosis ◽

Positive Predictive Value ◽

Negative Predictive Value ◽

Shear Wave ◽

Predictive Value ◽

Thyroid Nodules ◽

Shear Wave Elastography ◽

Roc Curves ◽

Diagnostic Value ◽

Sensitivity Specificity

Objective: To compare the diagnostic value of shear wave elastography (SWE) and real-time elastography (RTE) in the diagnosis of benign and malignant thyroid nodules. Methods: A total of 34 patients who ever received thyroidectomy in our hospital from January 2016 to January 2018 were identified. Meanwhile, all the patients received SWE and RTE before surgery, and all the diagnoses were confirmed by pathological examinations. With respect to SWE technique, the Subject Operating Characteristics (ROC) curves were drawn, in order to obtain the optimal threshold and then make differential diagnoses of benign and malignant thyroid nodules. In terms of RTE, the Rago 5 scoring method was utilized to make differential diagnoses of benign and malignant thyroid nodules. Besides, the pathological examinations after surgery could be considered as the golden standard. At last, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of SWE and RTE were calculated, respectively. Results: A total of 51 thyroid nodules were identified, and 41 nodules were benign, 10 nodules were malignant. On the basis of ROC curves, with respect to SWE, the best threshold for differential diagnosis of benign and malignant thyroid nodules is 38.3 kPa. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of SWE were 72.7% (8/11), 85% (34/40), 82.4% (42/51), 68.4% (13/19), and 87.5% (35/40), respectively. And the diagnostic indicators of RTE were 81.8% (9/11), 87.5% (35/40), 84.3% (43/51), 73.7% (14/19), and 90.0% (36/40). The sensitivity of quasi-static elastography in differential diagnosis of benign and malignant thyroid nodules with diameter ≤1 cm was 87.5% (7/8), and the sensitivity of SWE was 50.0% (5/10). In addition, the accuracy of SWE in differential diagnosis of benign and malignant thyroid nodules with diameter ≥3 cm was 100% (6/6), and the accuracy of RTE for this kind of thyroid nodules was 66.7% (4/6). Conclusion: Both SWE and RTE technology have good application value in differential diagnosis of benign and malignant thyroid nodules. But, SWE is preferable when making diagnosis of benign and malignant thyroid nodules with diameter ≥3 cm, and RTE was superior in detecting benign and malignant thyroid nodules with diameter ≤1 cm.

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The diagnostic accuracy of wrist cineradiography in diagnosing scapholunate dissociation

Journal of Hand Surgery (European Volume) ◽

10.1177/1753193413489056 ◽

2013 ◽

Vol 39 (3) ◽

pp. 263-271 ◽

Cited By ~ 20

Author(s):

G. S. I. Sulkers ◽

N. W. L. Schep ◽

M. Maas ◽

C. M. A. M. van der Horst ◽

J. C. Goslings ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Predictive Value ◽

Diagnostic Value ◽

Inclusion Criteria ◽

Scapholunate Ligament ◽

Scapholunate Dissociation ◽

Post Test ◽

Sensitivity Specificity ◽

Post Test Probability ◽

Test Probability

Ruptures of the scapholunate ligament (SLL) may cause carpal instability, also known as scapholunate dissociation (SLD). SLD may lead to osteoarthritis of the radiocarpal and midcarpal joints. The aim of this retrospective study was to determine the diagnostic value of wrist cineradiography in detecting SLD. All cineradiographic studies made during a 24 year period were retrieved. All patients who underwent the confirmation method (arthroscopy and/or arthrotomy) and cineradiography were included. In total, 84 patients met the inclusion criteria. Sensitivity, specificity, likelihood ratio, positive predictive value, negative predictive value, and diagnostic accuracy for detecting SLD were calculated for radiography and cineradiography. Cineradiography had a sensitivity of 90%, a specificity of 97%, and a diagnostic accuracy of 0.93 in detecting SLD. Radiography had a sensitivity of 81%, a specificity of 80%, and a diagnostic accuracy of 0.81. Cineradiography has a high diagnostic value for diagnosing SLDs. A positive cineradiography markedly increases the post-test probability of SLD.

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Comparisons of deep learning algorithms for diagnosing bacterial keratitis via external eye photographs

Scientific Reports ◽

10.1038/s41598-021-03572-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ming-Tse Kuo ◽

Benny Wei-Yun Hsu ◽

Yi-Sheng Lin ◽

Po-Chiung Fang ◽

Hun-Ju Yu ◽

...

Keyword(s):

Deep Learning ◽

Diagnostic Accuracy ◽

Rural Areas ◽

Predictive Value ◽

Rapid Development ◽

Characteristic Curve ◽

Bacterial Keratitis ◽

Average Percentage ◽

Sensitivity Specificity ◽

Corneal Melting

AbstractBacterial keratitis (BK), a painful and fulminant bacterial infection of the cornea, is the most common type of vision-threatening infectious keratitis (IK). A rapid clinical diagnosis by an ophthalmologist may often help prevent BK patients from progression to corneal melting or even perforation, but many rural areas cannot afford an ophthalmologist. Thanks to the rapid development of deep learning (DL) algorithms, artificial intelligence via image could provide an immediate screening and recommendation for patients with red and painful eyes. Therefore, this study aims to elucidate the potentials of different DL algorithms for diagnosing BK via external eye photos. External eye photos of clinically suspected IK were consecutively collected from five referral centers. The candidate DL frameworks, including ResNet50, ResNeXt50, DenseNet121, SE-ResNet50, EfficientNets B0, B1, B2, and B3, were trained to recognize BK from the photo toward the target with the greatest area under the receiver operating characteristic curve (AUROC). Via five-cross validation, EfficientNet B3 showed the most excellent average AUROC, in which the average percentage of sensitivity, specificity, positive predictive value, and negative predictive value was 74, 64, 77, and 61. There was no statistical difference in diagnostic accuracy and AUROC between any two of these DL frameworks. The diagnostic accuracy of these models (ranged from 69 to 72%) is comparable to that of the ophthalmologist (66% to 74%). Therefore, all these models are promising tools for diagnosing BK in first-line medical care units without ophthalmologists.

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Artificial Intelligent Stenosis Diagnosis in Coronary CTA: Effect on the Performance and Consistency of Readers with Less Cardiovascular Experience

10.21203/rs.3.rs-860518/v1 ◽

2021 ◽

Author(s):

Xianjun Han ◽

Nan Luo ◽

Lixue Xu ◽

Jiaxin Cao ◽

Ning Guo ◽

...

Keyword(s):

Predictive Value ◽

Diagnostic Performance ◽

Invasive Coronary Angiography ◽

Characteristic Curve ◽

Trial Registration ◽

Clinical Trial Registration ◽

Artificial Intelligent ◽

Atherosclerotic Stenosis ◽

Kappa Value ◽

Sensitivity Specificity

Abstract Background: To investigate the influence of artificial intelligent (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.Methods: We enrolled 196 patents who had undergone both CCTA and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1 to Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with aid from an AI system, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of six readers were calculated at the patient and vessel levels. Additionally, we evaluated interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6.Results: The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than Reader 5 and Reader 6. Reader 3 and Reader 4 aided by AI had a higher sensitivity (range: +7.2%~ +16.6% and +5.9%~ +16.1%, respectively) and NPV (range: +3.7%~ +13.4% and +2.7%~ +4.2%, respectively) than Reader 1 and Reader 2 without AI. There was good interobserver consistency between Reader 3 and Reader 4 in interpreting ≥50% stenosis (Kappa value= 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 had poor consistency (Kappa value= 0.25 and 0.37). Reader 5 and Reader 6 had moderate agreement (Kappa value= 0.55 and 0.61).Conclusions: Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve consistency in diagnosing coronary stenosis via CCTA.Trial registration: The clinical trial registration number: ChiCTR1900021867Name of registry: Diagnostic performance of artificial intelligence assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis

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Diagnostic accuracy of a urine dipstick for detecting albuminuria in hypertensive patients

F1000Research ◽

10.12688/f1000research.25564.2 ◽

2021 ◽

Vol 9 ◽

pp. 1244

Author(s):

Phornwipa Panta ◽

Win Techakehakij

Keyword(s):

Diagnostic Accuracy ◽

Predictive Value ◽

Operating Characteristic ◽

Characteristic Curve ◽

Hypertensive Patients ◽

Spot Urine ◽

Urine Dipstick ◽

Urine Albumin ◽

Low Sensitivity ◽

Sensitivity Specificity

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Contrast-Enhanced Ultrasound Improves the Accuracy of the ACR TI-RADS in the Diagnosis of Thyroid Nodules Located in the Isthmus

Ultraschall in der Medizin - European Journal of Ultrasound ◽

10.1055/a-1543-6033 ◽

2021 ◽

Author(s):

Yunlin Huang ◽

Yurong Hong ◽

Wen Xu ◽

Kai Song ◽

Pintong Huang

Keyword(s):

Diagnostic Performance ◽

Thyroid Nodules ◽

Characteristic Curve ◽

Diagnostic Value ◽

Contrast Enhanced Ultrasound ◽

Institutional Review ◽

Contrast Enhanced ◽

Significant Difference ◽

Image Reporting ◽

Benign Nodules

Abstract Objectives To evaluate the diagnostic performance of the American College of Radiology (ACR) Thyroid Image Reporting and Data System (TI-RADS), contrast-enhanced ultrasound (CEUS), and a modified TI-RADS in differentiating benign and malignant nodules located in the isthmus. Methods This retrospective study was approved by the institutional review board. Informed consent was obtained. Grayscale ultrasound (US) and CEUS images were obtained for 203 isthmic thyroid nodules (46 benign and 157 malignant) in 198 consecutive patients (156 women, mean age: 44.7 years ± 11.3 [standard deviation]; 47 men, mean age: 40.9 years ± 11.0). The area under the receiver operating characteristic curve (AUC) of the diagnostic performance of the ACR TI-RADS, CEUS, and the modified TI-RADS were evaluated. Results Lobulated or irregular margins (P = 0.001; odds ratio [OR] = 9.250) and punctate echogenic foci (P = 0.007; OR = 4.718) on US and hypoenhancement (P < 0.001; OR = 20.888) on CEUS displayed a significant association with malignancy located in the isthmus. The most valuable method to distinguish benign nodules from malignant nodules was the modified TI-RADS (AUC: 0.863 with modified TR5), which was significantly better than the ACR TI-RADS (AUC: 0.738 with ACR TR5) (P < 0.001) but showed no significant difference with respect to CEUS (AUC: 0.835 with hypoenhancement) (P = 0.205). The diagnostic value was significantly different between CEUS and the ACR TI-RADS (P = 0.028). Conclusion The modified TI-RADS could significantly improve the accuracy of the diagnosis of thyroid nodules located in the isthmus.

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Diagnostic Accuracy of Magnetic Resonance Images and Weight-Bearing Radiographs in Patients With Arthroscopic-Proven Medial Osteoarthritis of the Knee

Clinical Medicine Insights Arthritis and Musculoskeletal Disorders ◽

10.1177/1179544120938369 ◽

2020 ◽

Vol 13 ◽

pp. 117954412093836

Author(s):

Nuria Muñoz-García ◽

José Cordero-Ampuero ◽

Rosario Madero-Jarabo

Keyword(s):

Magnetic Resonance ◽

Diagnostic Accuracy ◽

Predictive Value ◽

Weight Bearing ◽

Diagnostic Value ◽

Magnetic Resonance Images ◽

Osteoarthritis Of The Knee ◽

Predictive Values ◽

Arthroscopic Findings ◽

Sensitivity Specificity

Aims: The aim of this study is to analyze the diagnostic value of weight-bearing radiographs, magnetic resonance images (MRI), and the combination of both in osteoarthritic knees when using arthroscopic findings as the “gold standard” to compare with. Methods: A total of 59 patients were studied because of chronic pain in 1 of their knees. Radiographs were classified according to Kellgren-Lawrence scale. Magnetic resonance images were classified according to Vallotton, and arthroscopic findings according to Outerbridge criteria. Results: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were, respectively, 75.0%, 60.0%, 56.2%, 77.8%, and 66.1% for weight-bearing radiographs, and 70.8%, 88.6%, 81.0%, 81.6%, and 81.4% for MRI. Logistic regression analysis showed that a weight-bearing radiograph added to MRI offered no additional diagnostic value compared with MRI alone ( P < .001). Conclusions: Magnetic resonance images presented higher specificity, positive and negative predictive values, and accuracy than weight-bearing radiographs for knee osteoarthritis. The combination of radiographs and MRI did not improve the diagnostic accuracy, compared with MRI alone.

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