Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data

Abstract Diagnostic errors are an internationally recognized patient safety concern, and leading causes are faulty data gathering and faulty information processing. Obtaining a full and accurate history from the patient is the foundation for timely and accurate diagnosis. A key concept underlying ideal history acquisition is “history clarification,” meaning that the history is clarified to be depicted as clearly as a video, with the chronology being accurately reproduced. A novel approach is presented to improve history-taking, involving six dimensions: Courtesy, Control, Compassion, Curiosity, Clear mind, and Concentration, the ‘6 C’s’. We report a case that illustrates how the 6C approach can improve diagnosis, especially in relation to artificial intelligence tools that assist with differential diagnosis.

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Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

BMC Cancer ◽

10.1186/s12885-020-07413-z ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Sihua Niu ◽

Jianhua Huang ◽

Jia Li ◽

Xueling Liu ◽

Dan Wang ◽

...

Keyword(s):

Artificial Intelligence ◽

Differential Diagnosis ◽

Breast Imaging ◽

Texture Features ◽

Matrix Analysis ◽

Width Ratio ◽

Malignant Group ◽

Malignant Lesions ◽

Occurrence Matrix ◽

Benign Group

Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

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A Training Program of Differential Diagnosis Skills Based on Virtual Reality and Artificial Intelligence

2010 Seventh International Conference on Information Technology: New Generations ◽

10.1109/itng.2010.107 ◽

2010 ◽

Author(s):

Jose Gutierrez-Maldonado ◽

Claudia Penaloza ◽

Adolfo Jarne ◽

Antoni Talarn ◽

Antonio Andres ◽

...

Keyword(s):

Artificial Intelligence ◽

Virtual Reality ◽

Differential Diagnosis ◽

Training Program

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Development of a Deep-Learning-Based Artificial Intelligence Tool for Differential Diagnosis between Dry and Neovascular Age-Related Macular Degeneration

Diagnostics ◽

10.3390/diagnostics10050261 ◽

2020 ◽

Vol 10 (5) ◽

pp. 261

Author(s):

Tae-Young Heo ◽

Kyoung Min Kim ◽

Hyun Kyu Min ◽

Sun Mi Gu ◽

Jae Hyun Kim ◽

...

Keyword(s):

Artificial Intelligence ◽

Differential Diagnosis ◽

Deep Learning ◽

Macular Degeneration ◽

Data Augmentation ◽

Treatment Strategies ◽

Image Data ◽

Age Related Macular Degeneration ◽

Age Related ◽

Leave One Out

The use of deep-learning-based artificial intelligence (AI) is emerging in ophthalmology, with AI-mediated differential diagnosis of neovascular age-related macular degeneration (AMD) and dry AMD a promising methodology for precise treatment strategies and prognosis. Here, we developed deep learning algorithms and predicted diseases using 399 images of fundus. Based on feature extraction and classification with fully connected layers, we applied the Visual Geometry Group with 16 layers (VGG16) model of convolutional neural networks to classify new images. Image-data augmentation in our model was performed using Keras ImageDataGenerator, and the leave-one-out procedure was used for model cross-validation. The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents. These results suggest the efficacy of this tool for early differential diagnosis of AMD in situations involving shortages of ophthalmology specialists and other medical devices.

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Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI

Radiology ◽

10.1148/radiol.2020190283 ◽

2020 ◽

Vol 295 (3) ◽

pp. 626-637 ◽

Cited By ~ 9

Author(s):

Andreas M. Rauschecker ◽

Jeffrey D. Rudie ◽

Long Xie ◽

Jiancong Wang ◽

Michael Tran Duong ◽

...

Keyword(s):

Artificial Intelligence ◽

Differential Diagnosis ◽

Brain Mri ◽

Artificial Intelligence System ◽

Intelligence System ◽

Diagnosis Accuracy

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Kraepelin: an expert system for the diagnosis of Obsessive - Compulsive Disorders

European Psychiatry ◽

10.1017/s0924933800002583 ◽

1991 ◽

Vol 6 (4) ◽

pp. 171-175 ◽

Cited By ~ 4

Author(s):

M Roca-Bennasar ◽

A Garcia-Mas ◽

N Llaneras ◽

J Blat ◽

P Roca

Keyword(s):

Artificial Intelligence ◽

Differential Diagnosis ◽

Expert System ◽

Natural Language ◽

Personal Computer ◽

Clinical History ◽

User Acceptance ◽

Obsessive Compulsive ◽

Obsessive Compulsive Disorders ◽

Compulsive Disorders

SummaryWe present the construction of an expert system (ES) for the diagnosis of Obsessive-Compulsive Disorders (OCD). It concerns an artificial intelligence tool, in Lisp language compatible with any personal computer (PC) with a hard disk. The ES asks the user 50 questions in natural language, on the patient or on a clinical history. It is provided with 115 rules of reasoning. Using single or multivaluate variables, the ES reaches the diagnosis of the Obsessive-Compulsive Disorders or the recommendations of differential diagnosis with related patterns or involucred with obsessive pathology: phobic, affective, schizophrenic and Gilles de la Tourette disorders. Finally, the perspectives for the utilisation of the ES in psychopathology are disscussed, in conjunction with the 2 serious problems created, design difficulty and user acceptance.

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Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia

Diagnostics ◽

10.3390/diagnostics11030530 ◽

2021 ◽

Vol 11 (3) ◽

pp. 530

Author(s):

Christian Salvatore ◽

Matteo Interlenghi ◽

Caterina B. Monti ◽

Davide Ippolito ◽

Davide Capra ◽

...

Keyword(s):

Artificial Intelligence ◽

Differential Diagnosis ◽

Area Under The Curve ◽

Community Acquired Pneumonia ◽

Validation Dataset ◽

X Rays ◽

Different Types ◽

Chest X Ray ◽

Sensitivity Specificity

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

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