Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review.

Background: Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. With advances in Artificial Intelligence (AI), tools have emerged that can facilitate diagnosis and classify dermatological images, complementing traditional clinical assessments and being applicable where there is a shortage of specialists. Its adoption requires analysis of efficacy, safety, and ethical considerations, as well as considering the genetic and ethnic diversity of patients. Objective: The systematic review aims to examine research on the detection, classification, and assessment of skin cancer images in clinical settings. Methods: We conducted a systematic literature search on PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis. Results: Through the search, 760 studies were identified in four databases, from which only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation. Conclusion: The application of artificial intelligence in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.

Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists' diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.

Motor Cortex Activation During Writing in Focal Upper-Limb Dystonia: An fNIRS Study.

BACKGROUND: Functional imaging studies have associated dystonia with abnormal activation in motor and sensory brain regions. Commonly used techniques such as functional magnetic resonance imaging impose physical constraints, limiting the experimental paradigms. Functional near-infrared spectroscopy (fNIRS) offers a new noninvasive possibility for investigating cortical areas and the neural correlates of complex motor behaviors in unconstrained settings. METHODS: We compared the cortical brain activation of patients with focal upper-limb dystonia and controls during the writing task under naturalistic conditions using fNIRS. The primary motor cortex (M1), the primary somatosensory cortex (S1), and the supplementary motor area were chosen as regions of interest (ROIs) to assess differences in changes in both oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) between groups. RESULTS: Group average activation maps revealed an expected pattern of contralateral recruitment of motor and somatosensory cortices in the control group and a more bilateral pattern of activation in the dystonia group. Between-group comparisons focused on specific ROIs revealed an increased activation of the contralateral M1 and S1 cortices and also of the ipsilateral M1 cortex in patients. CONCLUSIONS: Overactivity of contralateral M1 and S1 in dystonia suggest a reduced specificity of the task-related cortical areas, whereas ipsilateral activation possibly indicates a primary disorder of the motor cortex or an endophenotypic pattern. To our knowledge, this is the first study using fNIRS to assess cortical activity in dystonia during the writing task under natural settings, outlining the potential of this technique for monitoring sensory and motor retraining in dystonia rehabilitation.