scholarly journals Hearing Artificial Intelligence: Sonification Guidelines & Results From a Case-study in Melanoma Diagnosis

2019 ◽  
Author(s):  
R. Michael Winters ◽  
Ankur Kalra ◽  
Bruce N. Walker
Keyword(s):  
Artificial Intelligence ◽  
Data Driven ◽  
Learning Effects ◽  
General Technique ◽  
Melanoma Diagnosis ◽  
The Mean ◽  
Sensitivity Specificity ◽  
Improved Accuracy ◽  
Full Classification

The applications of artificial intelligence are becoming more and more prevalent in everyday life. Although many AI systems can operate autonomously, their goal is often assisting humans. Knowledge from the AI system must somehow be perceptualized. Towards this goal, we present a case-study in the application of data-driven non-speech audio for melanoma diagnosis. A physician photographs a suspicious skin lesion, triggering a sonification of the system’s penultimate classification layer. We iterated on sonification strategies and coalesced around designs representing three general approaches. We tested each in a group of novice listeners (n=7) for mean sensitivity, specificity, and learning effects. The mean accuracy was greatest for a simple model, but a trained dermatologist preferred a perceptually compressed model of the full classification layer. We discovered that training the AI on sonifications from this model improved accuracy further. We argue for perceptual compression as a general technique and for a comprehensible number of simultaneous streams.

scholarly journals Spectrum bias in algorithms derived by artificial intelligence: a case study in detecting aortic stenosis using electrocardiograms

2021 ◽  
Author(s):  
Andrew S Tseng ◽  
Michal Shelly-Cohen ◽  
Itzhak Z Attia ◽  
Peter A Noseworthy ◽  
Paul A Friedman ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Aortic Stenosis ◽  
Test Performance ◽  
Severe Aortic Stenosis ◽  
Model Performance ◽  
Target Population ◽  
Artificial Intelligence Algorithm ◽  
Sensitivity Specificity ◽  
Spectrum Model

Abstract Aims Spectrum bias can arise when a diagnostic test is derived from study populations with different disease spectra than the target population, resulting in poor generalizability. We used a real-world artificial intelligence-derived algorithm to detect severe aortic stenosis to experimentally assess the effect of spectrum bias on test performance. Methods and Results All adult patients at the Mayo Clinic between January 1st, 1989 to September 30th, 2019 with transthoracic echocardiograms within 180 days after electrocardiogram were identified. Two models were developed from two distinct patient cohorts: a whole-spectrum cohort comparing severe AS to any non-severe AS and an extreme-spectrum cohort comparing severe AS to no AS at all. Model performance was assessed. Overall, 258,607 patients had valid ECG and echocardiograms pairs. The area under the receiver operator curve was 0.87 and 0.91 for the whole-spectrum and extreme-spectrum models respectively. Sensitivity and specificity for the whole-spectrum model was 80% and 81% respectively, while for the extreme-spectrum model it was 84% and 84% respectively. When applying the AI-ECG derived from the extreme-spectrum cohort to patients in the whole-spectrum cohort, the sensitivity, specificity and AUC dropped to 83%, 73% and 0.86 respectively. Conclusion While the algorithm performed robustly in identifying severe AS, this study shows that limiting datasets to clearly positive or negative labels leads to overestimation of test performance when testing an artificial intelligence algorithm in the setting of classifying severe AS using ECG data. While the effect of the bias may be modest in this example, clinicians should be aware of the existence of such a bias in AI-derived algorithms.

scholarly journals Artificial intelligence can assist with diagnosing retinal vein occlusion

2021 ◽  
Vol 14 (12) ◽  
pp. 1895-1902
Author(s):  
Qiong Chen ◽  
◽  
Song Lin ◽  
Bo-Shi Liu ◽  
Yong Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Retinal Vein Occlusion ◽  
Area Under The Curve ◽  
Lesion Segmentation ◽  
Hard Exudates ◽  
Vein Occlusion ◽  
The Mean ◽  
Sensitivity Specificity ◽  
Mean Sensitivity ◽  
Segmentation Models

AIM: To assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible. METHODS: A total of 8600 color fundus photographs (CFPs) were included for training, validation, and testing of disease recognition models and lesion segmentation models. Four disease recognition and four lesion segmentation models were established and compared. Finally, one disease recognition model and one lesion segmentation model were selected as superior. Additionally, 224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models. RESULTS: Using the Inception-v3 model for disease identification, the mean sensitivity, specificity, and F1 for the three disease types and normal CFPs were 0.93, 0.99, and 0.95, respectively, and the mean area under the curve (AUC) was 0.99. Using the DeepLab-v3 model for lesion segmentation, the mean sensitivity, specificity, and F1 for four lesion types (abnormally dilated and tortuous blood vessels, cotton-wool spots, flame-shaped hemorrhages, and hard exudates) were 0.74, 0.97, and 0.83, respectively. CONCLUSION: DL models show good performance when recognizing RVO and identifying lesions using CFPs. Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists, DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.

The Oxford Handbook of Ethics of AI

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Rapid Development ◽  
Data Driven ◽  
International Human Rights Law ◽  
Human Knowledge ◽  
Human Rights Law ◽  
Ai Ethics ◽  
And Behavior ◽  
Control Complex

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

scholarly journals Digital Storytelling in Cultural Heritage: Audience Engagement in the Interactive Documentary New Life

2021 ◽  
Vol 13 (3) ◽  
pp. 1193
Author(s):  
Anna Podara ◽  
Dimitrios Giomelakis ◽  
Constantinos Nicolaou ◽  
Maria Matsiola ◽  
Rigas Kotsakis
Keyword(s):  
Cultural Heritage ◽  
Full Range ◽  
Social Dimension ◽  
Data Driven ◽  
Specific Interest ◽  
Key Factors ◽  
Audience Engagement ◽  
Main Research ◽  
The Social

This paper casts light on cultural heritage storytelling in the context of interactive documentary, a hybrid media genre that employs a full range of multimedia tools to document reality, provide sustainability of the production and successful engagement of the audience. The main research hypotheses are enclosed in the statements: (a) the interactive documentary is considered a valuable tool for the sustainability of cultural heritage and (b) digital approaches to documentary storytelling can provide a sustainable form of viewing during the years. Using the Greek interactive documentary (i-doc) NEW LIFE (2013) as a case study, the users’ engagement is evaluated by analyzing items from a seven-year database of web metrics. Specifically, we explore the adopted ways of the interactive documentary users to engage with the storytelling, the depth to which they were involved along with the most popular sections/traffic sources and finally, the differences between the first launch period and latest years were investigated. We concluded that interactivity affordances of this genre enhance the social dimension of cultural, while the key factors for sustainability are mainly (a) constant promotion with transmedia approach; (b) data-driven evaluation and reform; and (c) a good story that gathers relevant niches, with specific interest to the story.

scholarly journals Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study

2021 ◽  
Vol 296 ◽  
pp. 126242
Author(s):  
Oliver J. Fisher ◽  
Nicholas J. Watson ◽  
Laura Porcu ◽  
Darren Bacon ◽  
Martin Rigley ◽  
...  
Keyword(s):  
Data Driven ◽  
Multiple Target ◽  
Process Manufacturing ◽  
Target Data

Can the Expiratory Disproportion Index Distinguish PVFMD from Subglottic Stenosis in Obese Patients?

2021 ◽  
pp. 000348942199015
Author(s):  
Kevin Calamari ◽  
Stephen Politano ◽  
Laura Matrka
Keyword(s):  
Vocal Fold ◽  
Forced Expiratory Volume ◽  
Subglottic Stenosis ◽  
Obese Patients ◽  
Level Of Evidence ◽  
Prior Literature ◽  
The Mean ◽  
Sensitivity Specificity ◽  
Paradoxical Vocal Fold Movement ◽  
Rule Out

Objectives: Expiratory disproportion index (EDI) is the ratio of forced expiratory volume in 1 second (FEV1) divided by peak expiratory flow rate (PEFR) multiplied by 100. Prominent EDI (>50) values can differentiate subglottic stenosis (SGS) from paradoxical vocal fold movement disorder (PVFMD), but this has not been verified when considering body habitus. We hypothesize that the predictive value of elevated EDI in differentiating SGS from PVFMD will be lower in obese patients than non-obese patients. Methods: Patients ≥ 18 years old with recorded PFT values, BMI, and airway imaging were reviewed retrospectively from 01/2011 to 10/2018. EDI was recorded for 4 cohorts: non-obese/SGS, non-obese/ PVFMD, obese/SGS, and obese/ PVFMD, to determine the mean EDI and the sensitivity/specificity of an elevated EDI. Results: Mean EDI values were 69.32 and 48.38 in the non-obese SGS and PVFMD groups, respectively ( P < .01). They were 58.89 and 47.67 in the obese SGS and PVFMD groups, respectively ( P < .05). At a threshold of >50, EDI had a sensitivity of 90.0% and specificity of 51.6% in differentiating between SGS and PVFMD cases in non-obese patients and 51.6% and 63.6% in obese patients. Conclusion: Prior literature has established that EDI can distinguish SGS from PVFMD in the general population. Our results show that the mean EDI values were significantly different in both cohorts, but an elevated EDI was not as sensitive at identifying SGS cases in obese patients. This suggests that the EDI should be used with caution in obese patients and should not be relied upon to rule out SGS. Level of Evidence: 3.

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